scml.std

Subpackages

• scml.std.agents
  • scml.std.agents.aspiration
  • scml.std.agents.greedy
  • scml.std.agents.nothing
  • scml.std.agents.rand
• scml.std.rl
  • scml.std.rl.action
  • scml.std.rl.agent
  • scml.std.rl.common
  • scml.std.rl.env
  • scml.std.rl.observation
  • scml.std.rl.policies
  • scml.std.rl.reward

Submodules

• scml.std.agent
• scml.std.awi
• scml.std.common
• scml.std.context
• scml.std.policy
• scml.std.sysagents
• scml.std.ufun
• scml.std.world

Package Contents

Classes

StdAgent	Base class for all agents in the standard game.
StdSyncAgent	Base class for agents that negotiate synchronously by receiving all offers at once then responding to all of them at once.
EndingNegotiator	Base class for all SAO negotiators.
SingleAgreementAspirationAgent	Uses a time-based strategy to accept a single agreement from the set
GreedyStdAgent	A greedy agent based on StdAgent
GreedySyncAgent	A greedy agent based on OneShotSyncAgent
GreedyOneShotAgent	A greedy agent based on OneShotAgent
StdPlaceholder	An agent that always raises an exception if called to negotiate. It is useful as a placeholder (for example for RL and MARL exposition)
SyncRandomStdAgent	An agent that distributes its needs over its partners randomly.
SyncRandomOneShotAgent	An agent that distributes its needs over its partners randomly.
RandomStdAgent	A naive random agent
StdAWI	The agent world interface for the one-shot game.
StdState	State of a one-shot agent
StdExogenousContract	Exogenous contract information
StdProfile	Defines all private information of a factory
FinancialReport	A report published periodically by the system showing the financial standing of an agent
BaseStdContext	A context that generates std worlds with agents of a given types with predetermined structure and settings
GeneralStdContext	A context that generates std worlds with agents of a given types with predetermined structure and settings
FixedPartnerNumbersStdContext	Generates a world limiting the range of the agent level, production capacity
LimitedPartnerNumbersStdContext	Generates a oneshot world limiting the range of the agent level, production capacity
ANACStdContext	Generates a oneshot world with no constraints except compatibility with a specific ANAC competition year.
SupplierStdContext	A world context that can generate any world compatible with the observation manager
StrongSupplierStdContext	A supplier with almost many consumers relative to competitors
BalancedSupplierStdContext	A supplier with almost same number of consumers as competitors
WeakSupplierStdContext	A supplier with few consumers relative to competitors
MiddleManStdContext	A world context that can generate any world compatible with the observation manager
StrongMiddleManStdContext	A supplier with almost many consumers relative to competitors
BalancedMiddleManStdContext	A supplier with almost same number of consumers as competitors
WeakMiddleManStdContext	A supplier with few consumers relative to competitors
ConsumerStdContext	A world context that can generate any world compatible with the observation manager
StrongConsumerStdContext	A consumer with almost many suppliers relative to competitors
BalancedConsumerStdContext	A consumer with almost same number of suppliers as competitors
WeakConsumerStdContext	A consumer with few suppliers relative to competitors
StdContext	A basic context fixing stationary world config parameters
RepeatingStdContext	Encapsulates one or more configs and switches between them when asked to generate or make something.
StdPolicy	A std agent structured in three components, state encoder, policy (action) and action decoder.
ActionManager	Manges actions of an agent in an RL environment.
FlexibleActionManager	An action manager that matches any context.
StdEnv	The main Gymnasium class for implementing Reinforcement Learning Agents environments.
ObservationManager	Manages the observations of an agent in an RL environment
FlexibleObservationManager	An observation manager that can be used with any SCML world.
RewardFunction	Represents a reward function.
DefaultRewardFunction	The default reward function of SCML
DefaultStdAdapter	The base class of all agents running in Std based on StdAgent.
_StdSystemAgent	Implements an agent for handling system operations
StdUFun	Calculates the utility function of a list of contracts or offers.
UtilityInfo
StdWorld	The world representing the base standard simulation (starting SCML 2024)
SCML2024StdWorld	The SCML-standard simulation as used in [SCML 2024](https://scml.cs.brown.edu)

Functions

is_system_agent(→ bool)	Checks whether an agent is a system agent or not
is_system_agent(→ bool)	Checks whether an agent is a system agent or not
model_wrapper(→ RLModel)	Wraps a stable_baselines3 model as an RL model
random_action(→ numpy.ndarray)	Samples a random action from the action space of the
random_policy(→ numpy.ndarray)	Ends the negotiation or accepts with a predefined probability or samples a random response.
greedy_policy()[, debug])	A simple greedy policy.
builtin_agent_types([as_str])	Returns all built-in agents.

Attributes

__all__
StdDoNothingAgent
QUANTITY	Index of quantity in negotiation issues
UNIT_PRICE	Index of unit price in negotiation issues
TIME	Index of time in negotiation issues
INFINITE_COST	A constant indicating an invalid cost for lines incapable of running some process
SYSTEM_BUYER_ID	ID of the system buyer agent
SYSTEM_SELLER_ID	ID of the system seller agent
__all__
DefaultActionManager	The default action manager
StdRLAgent
RLState	We assume that RL states are numpy arrays
RLAction	We assume that RL actions are numpy arrays
RLModel	A policy is a callable that receives a state and returns an action
DefaultObservationManager	The default observation manager
UFunLimit	Information about one utility limit (either highest or lowest). See OnShotUFun.find_limit for details.
STD_DEFAULT_PARAMS
STD_DEFAULT_PARAMS2024
PLACEHOLDER_AGENT_PREFIX
__all__

class scml.std.StdAgent(owner=None, ufun: scml.oneshot.OneShotUFun | None = None, name=None)

Bases: scml.oneshot.agent.OneShotAgent

Base class for all agents in the standard game.

Remarks:

• You can access all of the negotiators associated with the agent using self.negotiators which is a dictionary mapping the negotiator_id to a tuple of two values: The SAONegotiator object and a key-value context dictionary.

• The negotiator_id associated with a negotiation with some partner will be the same as the agent ID of that partner. This means that all negotiators engaged with some partner over all simulation steps will have the same ID which is useful if you are keeping information about past negotiations and partner behavior.

class scml.std.StdSyncAgent(*args, **kwargs)

Bases: scml.oneshot.agent.OneShotSyncAgent, StdAgent

Base class for agents that negotiate synchronously by receiving all offers at once then responding to all of them at once.

class scml.std.EndingNegotiator(preferences: negmas.preferences.preferences.Preferences | None = None, ufun: negmas.preferences.base_ufun.BaseUtilityFunction | None = None, name: str | None = None, parent: negmas.negotiators.Controller | None = None, owner: negmas.situated.Agent | None = None, id: str | None = None, type_name: str | None = None, can_propose: bool = True, **kwargs)

Bases: negmas.sao.SAONegotiator, negmas.ControlledNegotiator

Base class for all SAO negotiators.

Parameters:

• name – Negotiator name

• parent – Parent controller if any

• preferences – The preferences of the negotiator

• ufun – The utility function of the negotiator (overrides preferences if given)

• owner – The Agent that owns the negotiator.

Remarks:

• The only method that must be implemented by any SAONegotiator is propose.

• The default respond method, accepts offers with a utility value no less than whatever propose returns with the same mechanism state.

propose(state)

Propose an offer or None to refuse.

Parameters:

state – GBState giving current state of the negotiation.

Returns:

The outcome being proposed or None to refuse to propose

Remarks:

• This function guarantees that no agents can propose something with a utility value

respond(state, source=None)

Called to respond to an offer. This is the method that should be overriden to provide an acceptance strategy.

Parameters:

• state – a SAOState giving current state of the negotiation.

• source – The ID of the negotiator that gave this offer

Returns:

The response to the offer

Return type:

ResponseType

Remarks:

• The default implementation never ends the negotiation

• The default implementation asks the negotiator to propose`() and accepts the `offer if its utility was at least as good as the offer that it would have proposed (and above the reserved value).

• The current offer to respond to can be accessed through state.current_offer

scml.std.__all__

class scml.std.SingleAgreementAspirationAgent(*args, **kwargs)

Bases: scml.oneshot.agent.OneShotSyncAgent

Uses a time-based strategy to accept a single agreement from the set it is considering.

before_step()

Called at the beginning of every step.

Remarks:

• Use this for any proactive code that needs to be done every simulation step.

counter_all(offers, states)

Calculate a response to all offers from all negotiators (negotiator ID is the key).

Parameters:

• offers – Maps negotiator IDs to offers

• states – Maps negotiator IDs to offers AT the time the offers were made.

Returns:

A dictionary mapping negotiator ID to an SAOResponse. The response per agent consist of a tuple. In case of acceptance or ending the negotiation the second item of the tuple should be None. In case of rejection, the second item should be the counter offer.

Remarks:

• The response type CANNOT be WAIT.

• If the system determines that a loop is formed, the agent may

receive this call for a subset of negotiations not all of them.

choose_agents(offers, outcome)

Selects an appropriate way to distribute this outcome to agents with given IDs.

first_proposals() → Dict[str, negmas.Outcome | None]

Gets a set of proposals to use for initializing the negotiation.

Returns:

A dictionary mapping each negotiator (in self.negotiators dict) to an outcome to be used as the first proposal if the agent is to start a negotiation.

class scml.std.GreedyStdAgent(*args, concession_exponent=None, acc_price_slack=float('inf'), step_price_slack=None, opp_price_slack=None, opp_acc_price_slack=None, range_slack=None, future_threshold=0.9, production_target=0.75, **kwargs)

Bases: scml.std.agent.StdAgent

A greedy agent based on StdAgent

Parameters:

• concession_exponent – A real number controlling how fast does the agent concede on price.

• acc_price_slack – The allowed slack in price limits compared with best prices I got so far

• step_price_slack – The allowed slack in price limits compared with best prices I got this step

• opp_price_slack – The allowed slack in price limits compared with best prices I got so far from a given opponent in this step

• opp_acc_price_slack – The allowed slack in price limits compared with best prices I got so far from a given opponent so far

• range_slack – Always consider prices above (1-range_slack) of the best possible prices good enough.

• production_target – Fraction of production capacity to be secured in advance

Remarks:

• A concession_exponent greater than one makes the agent concede super linearly and vice versa

init()

Initialize the quantities and best prices received so far

before_step()

Initialize the quantities and best prices received for next step

on_negotiation_success(contract, mechanism)

Record sales/supplies secured

propose(negotiator_id: str, state, source=None) → negmas.Outcome | None

Proposes an offer to one of the partners.

Parameters:

• negotiator_id – ID of the negotiator (and partner)

• state – Mechanism state including current step

Returns:

an outcome to offer.

respond(negotiator_id, state, source=None) → negmas.ResponseType

Responds to an offer from one of the partners.

Parameters:

• negotiator_id – ID of the negotiator (and partner)

• state – Mechanism state including current step

Returns:

A response type which can either be reject, accept, or end negotiation.

Remarks:

default behavior is to accept only if the current offer is the same or has a higher utility compared with what the agent would have proposed in the given state and reject otherwise

best_offer(negotiator_id)

_future_needs(negotiator_id, t)

_needed(negotiator_id)

_is_selling(nmi)

_is_good_price(nmi, state, offer)

Checks if a given price is good enough at this stage

_find_good_price(nmi, state, offer)

Finds a good-enough price conceding linearly over time

_price_range(nmi, offer)

Limits the price by the best price received

_th(step, n_steps)

calculates a descending threshold (0 <= th <= 1)

class scml.std.GreedySyncAgent(*args, threshold=None, **kwargs)

Bases: scml.oneshot.agent.OneShotSyncAgent, GreedyOneShotAgent

A greedy agent based on OneShotSyncAgent

before_step()

Called at the beginning of every step.

Remarks:

• Use this for any proactive code that needs to be done every simulation step.

first_proposals()

Decide a first proposal on every negotiation. Returning None for a negotiation means ending it.

counter_all(offers, states) → dict

Respond to a set of offers given the negotiation state of each.

_needs()

Returns both input and output needs

propose(negotiator_id, state)

Proposes an offer to one of the partners.

Parameters:

• negotiator_id – ID of the negotiator (and partner)

• state – Mechanism state including current step

Returns:

an outcome to offer.

respond(negotiator_id, state, source='')

Responds to an offer from one of the partners.

Parameters:

• negotiator_id – ID of the negotiator (and partner)

• state – Mechanism state including current step

Returns:

A response type which can either be reject, accept, or end negotiation.

Remarks:

default behavior is to accept only if the current offer is the same or has a higher utility compared with what the agent would have proposed in the given state and reject otherwise

class scml.std.GreedyOneShotAgent(*args, concession_exponent=None, acc_price_slack=float('inf'), step_price_slack=None, opp_price_slack=None, opp_acc_price_slack=None, range_slack=None, **kwargs)

Bases: scml.oneshot.agent.OneShotAgent

A greedy agent based on OneShotAgent

Parameters:

• concession_exponent – A real number controlling how fast does the agent concede on price.

• acc_price_slack – The allowed slack in price limits compared with best prices I got so far

• step_price_slack – The allowed slack in price limits compared with best prices I got this step

• opp_price_slack – The allowed slack in price limits compared with best prices I got so far from a given opponent in this step

• opp_acc_price_slack – The allowed slack in price limits compared with best prices I got so far from a given opponent so far

• range_slack – Always consider prices above (1-range_slack) of the best possible prices good enough.

Remarks:

• A concession_exponent greater than one makes the agent concede super linearly and vice versa

init()

Initialize the quantities and best prices received so far

before_step()

Initialize the quantities and best prices received for next step

on_negotiation_success(contract, mechanism)

Record sales/supplies secured

propose(negotiator_id: str, state, source=None) → negmas.Outcome | None

Proposes an offer to one of the partners.

Parameters:

• negotiator_id – ID of the negotiator (and partner)

• state – Mechanism state including current step

Returns:

an outcome to offer.

respond(negotiator_id, state, source=None) → negmas.ResponseType

Responds to an offer from one of the partners.

Parameters:

• negotiator_id – ID of the negotiator (and partner)

• state – Mechanism state including current step

Returns:

A response type which can either be reject, accept, or end negotiation.

Remarks:

default behavior is to accept only if the current offer is the same or has a higher utility compared with what the agent would have proposed in the given state and reject otherwise

best_offer(negotiator_id)

_needed(negotiator_id)

_is_selling(nmi)

_is_good_price(nmi, state, price)

Checks if a given price is good enough at this stage

_find_good_price(nmi, state)

Finds a good-enough price conceding linearly over time

_price_range(nmi)

Limits the price by the best price received

_th(step, n_steps)

calculates a descending threshold (0 <= th <= 1)

scml.std.StdDoNothingAgent

class scml.std.StdPlaceholder(*args, **kwargs)

Bases: scml.std.policy.StdPolicy

An agent that always raises an exception if called to negotiate. It is useful as a placeholder (for example for RL and MARL exposition)

act(state)

The main policy. Generates an action given a state

class scml.std.SyncRandomStdAgent(*args, today_target_productivity=0.3, future_target_productivity=0.3, today_concentration=0.25, future_concentration=0.75, today_concession_exp=2.0, future_concession_exp=4.0, future_min_price=0.25, prioritize_near_future: bool = False, prioritize_far_future: bool = False, pfuture=0.15, **kwargs)

Bases: scml.std.agent.StdSyncAgent

An agent that distributes its needs over its partners randomly.

first_proposals()

Gets a set of proposals to use for initializing the negotiation.

Returns:

A dictionary mapping each negotiator (in self.negotiators dict) to an outcome to be used as the first proposal if the agent is to start a negotiation.

counter_all(offers, states)

Calculate a response to all offers from all negotiators (negotiator ID is the key).

Parameters:

• offers – Maps negotiator IDs to offers

• states – Maps negotiator IDs to offers AT the time the offers were made.

Returns:

A dictionary mapping negotiator ID to an SAOResponse. The response per agent consist of a tuple. In case of acceptance or ending the negotiation the second item of the tuple should be None. In case of rejection, the second item should be the counter offer.

Remarks:

• The response type CANNOT be WAIT.

• If the system determines that a loop is formed, the agent may

receive this call for a subset of negotiations not all of them.

distribute_todays_needs(partners=None) → dict[str, int]

Distributes my needs randomly over all my partners

estimate_future_needs()

Estimates how much I need to buy and sell for each future step

distribute_future_offers(partners: list[str]) → dict[str, negmas.Outcome | None]

Distribute future offers over the given partners

is_supplier(negotiator_id)

is_consumer(negotiator_id)

best_price(partner_id)

Best price for a negotiation today

good_price(partner_id, today: bool)

A good price to use

buy_price(t: float, mn: float, mx: float, today: bool) → float

Return a good price to buy at

sell_price(t: float, mn: float, mx: float, today: bool) → float

Return a good price to sell at

good2buy(p: float, t: float, mn, mx, today: bool)

Is p a good price to buy at?

good2sell(p: float, t: float, mn, mx, today: bool)

Is p a good price to sell at?

class scml.std.SyncRandomOneShotAgent(*args, equal: bool = False, overordering_max: float = 0.2, overordering_min: float = 0.0, overordering_exp: float = 0.4, mismatch_exp: float = 4.0, mismatch_max: float = 0.3, **kwargs)

Bases: scml.oneshot.agent.OneShotSyncAgent

An agent that distributes its needs over its partners randomly.

Parameters:

• equal – If given, it tries to equally distribute its needs over as many of its suppliers/consumers as possible

• overordering_max – Maximum fraction of needs to over-order. For example, it the agent needs 5 items and this is 0.2, it will order 6 in the first negotiation step.

• overordering_min – Minimum fraction of needs to over-order. Used in the last negotiation step.

• overordering_exp – Controls how fast does the over-ordering quantity go from max to min.

• concession_exp – Controls how fast does the agent concedes on matching its needs exactly.

• mismatch_max – Maximum mismtach in quantity allowed between needs and accepted offers. If a fraction, it is will be this fraction of the production capacity (n_lines).

init()

Called once after the AWI is set.

Remarks:

• Use this for any proactive initialization code.

distribute_needs(t: float) → dict[str, int]

Distributes my needs randomly over all my partners

first_proposals()

Gets a set of proposals to use for initializing the negotiation.

Returns:

A dictionary mapping each negotiator (in self.negotiators dict) to an outcome to be used as the first proposal if the agent is to start a negotiation.

counter_all(offers, states)

Calculate a response to all offers from all negotiators (negotiator ID is the key).

Parameters:

• offers – Maps negotiator IDs to offers

• states – Maps negotiator IDs to offers AT the time the offers were made.

Returns:

A dictionary mapping negotiator ID to an SAOResponse. The response per agent consist of a tuple. In case of acceptance or ending the negotiation the second item of the tuple should be None. In case of rejection, the second item should be the counter offer.

Remarks:

• The response type CANNOT be WAIT.

• If the system determines that a loop is formed, the agent may

receive this call for a subset of negotiations not all of them.

_allowed_mismatch(r: float)

_overordering_fraction(t: float)

_step_and_price(best_price=False)

Returns current step and a random (or max) price

class scml.std.RandomStdAgent(owner=None, ufun=None, name=None, p_accept=PROB_ACCEPTANCE, p_end=PROB_END)

Bases: scml.std.agent.StdAgent

A naive random agent

propose(negotiator_id: str, state: negmas.sao.SAOState) → negmas.Outcome | None

Proposes an offer to one of the partners.

Parameters:

• negotiator_id – ID of the negotiator (and partner)

• state – Mechanism state including current step

Returns:

an outcome to offer.

respond(negotiator_id, state, source=None)

Responds to an offer from one of the partners.

Parameters:

• negotiator_id – ID of the negotiator (and partner)

• state – Mechanism state including current step

Returns:

A response type which can either be reject, accept, or end negotiation.

Remarks:

default behavior is to accept only if the current offer is the same or has a higher utility compared with what the agent would have proposed in the given state and reject otherwise

class scml.std.StdAWI(world: scml.oneshot.world.SCMLBaseWorld, agent: scml.oneshot.agent.OneShotAgent)

Bases: scml.oneshot.awi.OneShotAWI

The agent world interface for the one-shot game.

This class contains all the methods needed to access the simulation to extract information which are divided into 4 groups:

Static World Information:

Information about the world and the agent that does not change over time. These include:

1. Market Information:

• n_products: Number of products in the production chain.

• n_processes: Number of processes in the production chain.

• n_competitors: Number of other factories on the same production level.

• all_suppliers: A list of all suppliers by product.

• all_consumers: A list of all consumers by product.

• proudction_capacities: The total production capacity (i.e. number of lines)

  for each production level (i.e. manufacturing process).

• is_system: Is the given system ID corresponding to a system agent?

• is_bankrupt: Is the given agent bankrupt? None asks about self

• catalog_prices: A list of the catalog prices (by product).

• price_multiplier: The multiplier multiplied by the trading/catalog price when the negotiation agendas are created to decide the maximum and lower quantities.

• is_exogenous_forced: Are exogenous contracts always forced or can the agent decide not to sign them.

• current_step: Current simulation step (inherited from negmas.situated.AgentWorldInterface ).

• n_steps: Number of simulation steps (inherited from negmas.situated.AgentWorldInterface ).

• relative_time: fraction of the simulation completed (inherited from negmas.situated.AgentWorldInterface).

• state: The full state of the agent ( OneShotState ).

• **settings* The system settings (inherited from negmas.situated.AgentWorldInterface ).

• **quantity_range* The maximum quantity in all negotiation agendas (new in 0.6.1)

• **price_range* The maximum number of different prices in any negotiation agenda (new in 0.6.1)

• **horizon* The negotiation horizon for delivery dates. A value greater than zero indicates that you can get agreements about future deliveries.

2. Agent Information:

• profile: Gives the agent profile including its production cost, number of production lines, input product index, mean of its delivery penalties, mean of its disposal costs, standard deviation of its shortfall penalties and standard deviation of its disposal costs. See OneShotProfile for full description. This information is private information and no other agent knows it.

• n_lines: the number of production lines in the factory (private information).

• is_first_level: Is the agent in the first production level (i.e. it is an input agent that buys the raw material).

• is_last_level: Is the agent in the last production level (i.e. it is an output agent that sells the final product).

• is_middle_level: Is the agent neither a first level nor a last level agent

• my_input_product: The input product to the factory controlled by the agent.

• my_output_product: The output product from the factory controlled by the agent.

• level: The production level which is numerically the same as the input product.

• my_suppliers: A list of IDs for all suppliers to the agent (i.e. agents that can sell the input product of the agent).

• my_consumers: A list of IDs for all consumers to the agent (i.e. agents that can buy the output product of the agent).

• penalties_scale: The scale at which to calculate disposal cost/delivery penalties. “trading” and “catalog” mean trading and catalog prices. “unit” means the contract’s unit price while “none” means that disposal cost/shortfall penalty are absolute.

• n_input_negotiations: Number of negotiations with suppliers.

• n_output_negotiations: Number of negotiations with consumers.

Dynamic World Information:

Information about the world and the agent that changes over time.

1. Market Information:

• trading_prices: The trading prices of all products. This information is only available if publish_trading_prices is set in the world.

• exogenous_contract_summary: A list of n_products tuples each giving the total quantity and average price of exogenous contracts for a product. This information is only available if publish_exogenous_summary is set in the world.

• is_perishable: Are all products perishable?

2. Other Agents’ Information:

• reports_of_agent: Gives all past financial reports of a given agent. See FinancialReport for details.

• reports_at_step: Gives all reports of all agents at a given step. See FinancialReport for details.

3. Current Negotiations Information:

• current_input_outcome_space: The current outcome-space for all negotiations to buy the input product of the agent. If the agent is at level zero, this will have no issues.

• current_output_outcome_space: The current outcome-space for all negotiations to buy the output product of the agent. If the agent is at level n_products - 1, this will have no issues.

• current_negotiation_details: Details on all current negotiations separated into “buy” and “sell” dictionaries.

Useful helpers about current negotiations:

• current_input_issues: The current issues for all negotiations to buy the input product of the agent. If the agent is at level zero, this will be empty. This is exactly the same as current_input_outcome_space.issues

• current_output_issues: The current issues for all negotiations to buy the output product of the agent. If the agent is at level n_products - 1, this will be empty. This is exactly the same as current_output_outcome_space.issues

• current_buy_nmis: All NMIs for current buy negotiations.

• current_sell_nmis: All NMIs for current sell negotiations.

• current_nmis: All states for current negotiations.

• current_buy_states: All states for current buy negotiations.

• current_sell_states: All states for current sell negotiations.

• current_states: All states for current negotiations.

• current_buy_offers: All offers for current buy negotiations.

• current_sell_offers: All offers for current sell negotiations.

• current_offers: All offers for current negotiations.

• running_buy_nmis: All NMIs for running buy negotiations.

• running_sell_nmis: All NMIs for running sell negotiations.

• running_nmis: All states for running negotiations.

• running_buy_states: All states for running buy negotiations.

• running_sell_states: All states for running sell negotiations.

• running_states: All states for running negotiations.

4. Agent Information:

• current_exogenous_input_quantity: The total quantity the agent have in its input exogenous contract.

• current_exogenous_input_price: The total price of the agent’s input exogenous contract.

• current_exogenous_output_quantity: The total quantity the agent have in its output exogenous contract.

• current_exogenous_output_price: The total price of the agent’s output exogenous contract

• current_disposal_cost: The disposal cost per unit item in the current step.

• current_shortfall_penalty: The shortfall penalty per unit item in the current step.

• current_balance: The current balance of the agent

• current_score: The current score (balance / initial balance) of the agent

• current_inventory_input: The total quantity remaining in the inventory of the input product

• current_inventory_output: The total quantity remaining in the inventory of the output product

• current_inventory: The total quantity remaining in the inventory of the input and output product

5. Sales and Supplies (quantities) for today:

• sales: Today’s sales per customer so far.

• supplies: Today’s supplies per supplier so far.

• total_sales: Today’s total sales so far.

• total_supplies: Today’s total supplies so far.

• needed_sales: Today’s needed sales as of now (exogenous input + total supplies - exogenous output - total sales so far).

• needed_supplies: Today’s needed supplies as of now (exogenous output + total sales - exogenous input - total supplies so far).

• future_sales: Future quantity of the output product in standing contracts not executed nor nullified.

• future_supplies: Future quantity of the input product in standing contracts not executed nor nullified.

• total_future_sales: Total future quantity of the output product in standing contracts not executed nor nullified.

• total_future_supplies: Total future quantity of the input product in standing contracts not executed nor nullified.

• total_future_sales_between: Total future sale quantities between the given two simulated days (non-exogenous).

• total_future_supplies_between: Total future supply quantities between the given two simulated days (non-exogenous).

• total_future_sales_until: Total future sale quantities between tomorrow and the given day (non-exogenous).

• total_future_supplies_until: Total future supply quantities between tomorrow and the given day (non-exogenous).

• total_future_sales_at: Total future sale quantities at the given day (non-exogenous).

• total_future_supplies_at: Total future supply quantities at the given day (non-exogenous).

• future_sales_cost: Future total_cost of the output product in standing contracts not executed nor nullified.

• future_supplies_cost: Future total cost of the input product in standing contracts not executed nor nullified.

Services (All inherited from negmas.situated.AgentWorldInterface):

• logdebug/loginfo/logwarning/logerror: Logs to the world log at the given log level.

• logdebug_agent/loginf_agnet/…: Logs to the agent specific log at the given log level.

• bb_query: Queries the bulletin-board.

• bb_read: Read a section of the bulletin-board.

scml.std.QUANTITY = 0

Index of quantity in negotiation issues

scml.std.UNIT_PRICE = 2

Index of unit price in negotiation issues

scml.std.TIME = 1

Index of time in negotiation issues

class scml.std.StdState

Bases: scml.oneshot.common.OneShotState

State of a one-shot agent

class scml.std.StdExogenousContract

Bases: scml.oneshot.common.OneShotExogenousContract

Exogenous contract information

class scml.std.StdProfile

Bases: scml.oneshot.common.OneShotProfile

Defines all private information of a factory

class scml.std.FinancialReport

A report published periodically by the system showing the financial standing of an agent

__slots__ = ['agent_id', 'step', 'cash', 'assets', 'breach_prob', 'breach_level', 'is_bankrupt', 'agent_name']

agent_id: str

Agent ID

step: int

Simulation step at the beginning of which the report was published.

cash: int

Cash in the agent’s wallet. Negative numbers indicate liabilities.

assets: int

Value of the products in the agent’s inventory @ catalog prices.

breach_prob: float

Number of times the agent breached a contract over the total number of contracts it signed.

breach_level: float

Sum of the agent’s breach levels so far divided by the number of contracts it signed.

is_bankrupt: bool

Whether the agent is already bankrupt (i.e. incapable of doing any more transactions).

agent_name: str

Agent name for printing purposes

__str__()

Return str(self).

scml.std.is_system_agent(aid: str) → bool

Checks whether an agent is a system agent or not

Parameters:

aid – Agent ID

Returns:

True if the ID is for a system agent.

scml.std.INFINITE_COST

A constant indicating an invalid cost for lines incapable of running some process

scml.std.SYSTEM_BUYER_ID = 'BUYER'

ID of the system buyer agent

scml.std.SYSTEM_SELLER_ID = 'SELLER'

ID of the system seller agent

scml.std.is_system_agent(aid: str) → bool

Checks whether an agent is a system agent or not

Parameters:

aid – Agent ID

Returns:

True if the ID is for a system agent.

class scml.std.BaseStdContext

Bases: scml.oneshot.context.BaseContext

A context that generates std worlds with agents of a given types with predetermined structure and settings

world_type: type[scml.oneshot.world.SCMLBaseWorld]

non_competitors: tuple[str | type[scml.oneshot.agent.OneShotAgent], Ellipsis]

class scml.std.GeneralStdContext

Bases: scml.oneshot.context.GeneralContext

A context that generates std worlds with agents of a given types with predetermined structure and settings

world_type: type[scml.oneshot.world.SCMLBaseWorld]

non_competitors: tuple[str | type[scml.oneshot.agent.OneShotAgent], Ellipsis]

perishable: bool = False

horizon: int

n_processes: tuple[int, int] | int

disposal_cost: tuple[float, float] | float

disposal_cost_dev: tuple[float, float] | float

storage_cost: tuple[float, float] | float

storage_cost_dev: tuple[float, float] | float

price_range_fraction: float | tuple[float, float]

price_multiplier: numpy.ndarray | tuple[float, float] | float

wide_price_range: bool

one_time_per_negotiation: bool

quantity_multiplier: int

max_productivity: tuple[float, float] | float

max_supply: tuple[float, float] | float

exogenous_supply_predictability: tuple[float, float] | float

exogenous_sales_predictability: tuple[float, float] | float

cap_exogenous_quantities: bool

class scml.std.FixedPartnerNumbersStdContext

Bases: scml.oneshot.context.FixedPartnerNumbersContext

Generates a world limiting the range of the agent level, production capacity and the number of suppliers, consumers, and optionally same-level competitors.

world_type: type[scml.oneshot.world.SCMLBaseWorld]

non_competitors: tuple[str | type[scml.oneshot.agent.OneShotAgent], Ellipsis]

class scml.std.LimitedPartnerNumbersStdContext

Bases: scml.oneshot.context.LimitedPartnerNumbersOneShotContext

Generates a oneshot world limiting the range of the agent level, production capacity and the number of suppliers, consumers, and optionally same-level competitors.

year: int = 2024

__attrs_post_init__()

class scml.std.ANACStdContext

Bases: GeneralStdContext

Generates a oneshot world with no constraints except compatibility with a specific ANAC competition year.

year: int = 2024

__attrs_post_init__()

class scml.std.SupplierStdContext(*args, **kwargs)

Bases: scml.oneshot.context.SupplierContext

A world context that can generate any world compatible with the observation manager

world_type: type[scml.oneshot.world.SCMLBaseWorld]

non_competitors: tuple[str | type[scml.oneshot.agent.OneShotAgent], Ellipsis]

class scml.std.StrongSupplierStdContext(*args, **kwargs)

Bases: SupplierStdContext

A supplier with almost many consumers relative to competitors

class scml.std.BalancedSupplierStdContext(*args, **kwargs)

Bases: SupplierStdContext

A supplier with almost same number of consumers as competitors

class scml.std.WeakSupplierStdContext(*args, **kwargs)

Bases: SupplierStdContext

A supplier with few consumers relative to competitors

class scml.std.MiddleManStdContext(*args, **kwargs)

Bases: scml.oneshot.context.LimitedPartnerNumbersOneShotContext

A world context that can generate any world compatible with the observation manager

class scml.std.StrongMiddleManStdContext(*args, **kwargs)

Bases: MiddleManStdContext

A supplier with almost many consumers relative to competitors

class scml.std.BalancedMiddleManStdContext(*args, **kwargs)

Bases: MiddleManStdContext

A supplier with almost same number of consumers as competitors

class scml.std.WeakMiddleManStdContext(*args, **kwargs)

Bases: MiddleManStdContext

A supplier with few consumers relative to competitors

class scml.std.ConsumerStdContext(*args, **kwargs)

Bases: scml.oneshot.context.ConsumerContext

A world context that can generate any world compatible with the observation manager

world_type: type[scml.oneshot.world.SCMLBaseWorld]

non_competitors: tuple[str | type[scml.oneshot.agent.OneShotAgent], Ellipsis]

class scml.std.StrongConsumerStdContext(*args, **kwargs)

Bases: ConsumerStdContext

A consumer with almost many suppliers relative to competitors

class scml.std.BalancedConsumerStdContext(*args, **kwargs)

Bases: ConsumerStdContext

A consumer with almost same number of suppliers as competitors

class scml.std.WeakConsumerStdContext(*args, **kwargs)

Bases: ConsumerStdContext

A consumer with few suppliers relative to competitors

class scml.std.StdContext

Bases: GeneralStdContext

A basic context fixing stationary world config parameters

class scml.std.RepeatingStdContext

Bases: scml.oneshot.context.RepeatingContext

Encapsulates one or more configs and switches between them when asked to generate or make something.

world_type: type[scml.oneshot.world.SCMLBaseWorld]

non_competitors: tuple[str | type[scml.oneshot.agent.OneShotAgent], Ellipsis]

class scml.std.StdPolicy(*args, **kwargs)

Bases: scml.oneshot.policy.OneShotPolicy

A std agent structured in three components, state encoder, policy (action) and action decoder.

The agent is divided into three components:

1. State encoder (encode_state()) which takes the current state of all negotiation mechanisms, access the awi as needed, and generates a state which can be of any type to be passed to the next component.

2. Policy (act()) which takes the state generated from the state encoder and returns an action which may be encoded as any type to be passed to the next component. The policy (i.e. `act` () method) is not supposed to access the AWI or any other members of the class. It is preferred to be a pure function. This makes it easy to test the policy at predefined conditions (i.e. states) without having to construct a simulation.

3. Action decoder (decode_action()) which takes the action generated from the policy and generates the appropriate set of responses to all partners.

Remarks:

• The simplest form of state encoder which is implemented by default is to return the state member of the AWI.

• The simplest form of action encoding is to simply return the responses as a dict[str, SAOResponse] from act which is then passed as it is by decode_action . This is the default implementation of decode_action

scml.std.__all__

class scml.std.ActionManager

Bases: abc.ABC

Manges actions of an agent in an RL environment.

context: scml.oneshot.context.BaseContext

continuous: bool = False

n_suppliers: int

n_consumers: int

n_partners: int

abstract make_space() → gymnasium.Space

Creates the action space

abstract decode(awi: scml.oneshot.awi.OneShotAWI, action: numpy.ndarray) → dict[str, negmas.sao.common.SAOResponse]

Decodes an action from an array to a PurchaseOrder and a CounterMessage.

encode(awi: scml.oneshot.awi.OneShotAWI, responses: dict[str, negmas.sao.common.SAOResponse]) → numpy.ndarray

Encodes an action as an array. This is only used for testing so it is optional

class scml.std.FlexibleActionManager

Bases: ActionManager

An action manager that matches any context.

Parameters:

• n_prices – Number of distinct prices allowed in the action.

• max_quantity – Maximum allowed quantity to offer in any negotiation. The number of quantities is one plus that because zero is allowed to model ending negotiation.

• n_partners – Maximum of partners allowed in the action.

Remarks:

• This action manager will always generate offers that are within the price and quantity limits given in its parameters. Wen decoding them, it will scale them up so that the maximum corresponds to the actual value in the world it finds itself. For example, if n_prices is 10 and the world has only two prices currently in the price issue, it will use any value less than 5 as the minimum price and any value above 5 as the maximum price. If on the other hand the current price issue has 20 values, then it will scale by multiplying the number given in the encoded action (ranging from 0 to 9) by 19/9 which makes it range from 0 to 19 which is what is expected by the world.

• This action manager will adjust offers for different number of partners as follows: - If the true number of partners is larger than n_partners used by this action manager,

  it will simply use n_partners of them and always end negotiations with the rest of them.

  • If the true number of partners is smaller than n_partners, it will use the first n_partners values in the encoded action and increase the quantities of any counter offers (i.e. ones in which the response is REJECT_OFFER) by the amount missing from the ignored partners in the encoded action up to the maximum quantities allowed by the current negotiation context. For example, if n_partneers is 4 and we have only 2 partners in reality, and the received quantities from partners were [4, 3] while the maximum quantity allowed is 10 and the encoded action was [2, *, 3, *, 2, *, 1, *] (where we ignored prices), then the encoded action will be converted to [(Reject, 5, *), (Accept, 3, *)] where the 3 extra units that were supposed to be offered to the last two partners are moved to the first partner. If the maximum quantity allowed was 4 in that example, the result will be [(Reject, 4, *), (Accept, 3, *)].

capacity_multiplier: int = 1

n_prices: int = 2

max_group_size: int = 2

reduce_space_size: bool = True

extra_checks: bool = False

max_quantity: int

__attrs_post_init__()

make_space() → gymnasium.spaces.MultiDiscrete | gymnasium.spaces.Box

Creates the action space

decode(awi: scml.oneshot.awi.OneShotAWI, action: numpy.ndarray) → dict[str, negmas.sao.common.SAOResponse]

Generates offers to all partners from an encoded action. Default is to return the action as it is assuming it is a dict[str, SAOResponse]

encode(awi: scml.oneshot.awi.OneShotAWI, responses: dict[str, negmas.sao.common.SAOResponse]) → numpy.ndarray

Receives offers for all partners and generates the corresponding action. Used mostly for debugging and testing.

scml.std.DefaultActionManager

The default action manager

scml.std.StdRLAgent

scml.std.RLState

We assume that RL states are numpy arrays

scml.std.RLAction

We assume that RL actions are numpy arrays

scml.std.RLModel

A policy is a callable that receives a state and returns an action

scml.std.model_wrapper(model, deterministic: bool = False) → RLModel

Wraps a stable_baselines3 model as an RL model

class scml.std.StdEnv(action_manager: scml.oneshot.rl.action.ActionManager, observation_manager: scml.oneshot.rl.observation.ObservationManager, reward_function: scml.oneshot.rl.reward.RewardFunction = DefaultRewardFunction(), render_mode=None, context: scml.oneshot.context.GeneralContext = FixedPartnerNumbersStdContext(), agent_type: type[scml.std.agent.StdAgent] = StdPlaceholder, agent_params: dict[str, Any] | None = None, extra_checks: bool = True, skip_after_negotiations: bool = True)

Bases: scml.oneshot.rl.env.OneShotEnv

The main Gymnasium class for implementing Reinforcement Learning Agents environments.

The class encapsulates an environment with arbitrary behind-the-scenes dynamics through the step() and reset() functions. An environment can be partially or fully observed by single agents. For multi-agent environments, see PettingZoo.

The main API methods that users of this class need to know are:

• step() - Updates an environment with actions returning the next agent observation, the reward for taking that actions, if the environment has terminated or truncated due to the latest action and information from the environment about the step, i.e. metrics, debug info.

• reset() - Resets the environment to an initial state, required before calling step. Returns the first agent observation for an episode and information, i.e. metrics, debug info.

• render() - Renders the environments to help visualise what the agent see, examples modes are “human”, “rgb_array”, “ansi” for text.

• close() - Closes the environment, important when external software is used, i.e. pygame for rendering, databases

Environments have additional attributes for users to understand the implementation

• action_space - The Space object corresponding to valid actions, all valid actions should be contained within the space.

• observation_space - The Space object corresponding to valid observations, all valid observations should be contained within the space.

• reward_range - A tuple corresponding to the minimum and maximum possible rewards for an agent over an episode. The default reward range is set to \((-\infty,+\infty)\).

• spec - An environment spec that contains the information used to initialize the environment from gymnasium.make()

• metadata - The metadata of the environment, i.e. render modes, render fps

• np_random - The random number generator for the environment. This is automatically assigned during super().reset(seed=seed) and when assessing self.np_random.

See also

For modifying or extending environments use the gymnasium.Wrapper class

Note

To get reproducible sampling of actions, a seed can be set with env.action_space.seed(123).

class scml.std.ObservationManager

Bases: Protocol

Manages the observations of an agent in an RL environment

property context: scml.oneshot.context.BaseContext

make_space() → gymnasium.spaces.Space

Creates the observation space

encode(awi: scml.oneshot.awi.OneShotAWI) → numpy.ndarray

Encodes an observation from the agent’s awi

make_first_observation(awi: scml.oneshot.awi.OneShotAWI) → numpy.ndarray

Creates the initial observation (returned from gym’s reset())

get_offers(awi: scml.oneshot.awi.OneShotAWI, encoded: numpy.ndarray) → dict[str, negmas.outcomes.Outcome | None]

Gets the offers from an encoded awi

class scml.std.FlexibleObservationManager

Bases: BaseObservationManager

An observation manager that can be used with any SCML world.

Parameters:

• capacity_multiplier – A factor to multiply by the number of lines to give the maximum quantity allowed in offers

• exogenous_multiplier – A factor to multiply maximum production capacity with when encoding exogenous quantities

• continuous – If given the observation space will be a Box otherwise it will be a MultiDiscrete

• n_prices – The number of prices to use for encoding the unit price (if not continuous)

• max_production_cost – The limit for production cost. Anything above that will be mapped to this max

• max_group_size – Maximum size used for grouping observations from multiple partners. This will be used in the number of partners in the simulation is larger than the number used for training.

• n_past_received_offers – Number of past received offers to add to the observation.

• n_bins –

  14. bins to use for discretization (if not continuous)

• n_sigmas – The number of sigmas used for limiting the range of randomly distributed variables

• extra_checks – If given, extra checks are applied to make sure encoding and decoding make sense

Remarks:

…

capacity_multiplier: int = 1

n_prices: int = 2

max_group_size: int = 2

reduce_space_size: bool = True

n_past_received_offers: int = 1

extra_checks: bool = False

n_bins: int = 40

n_sigmas: int = 2

max_production_cost: int = 10

exogenous_multiplier: int = 1

max_quantity: int

_chosen_partner_indices: list[int] | None

_previous_offers: collections.deque

_dims: list[int] | None

__attrs_post_init__()

get_dims() → list[int]

Get the sizes of all dimensions in the observation space. Used if not continuous.

make_space() → gymnasium.spaces.MultiDiscrete | gymnasium.spaces.Box

Creates the action space

make_first_observation(awi: scml.oneshot.awi.OneShotAWI) → numpy.ndarray

Creates the initial observation (returned from gym’s reset())

encode(awi: scml.oneshot.awi.OneShotAWI) → numpy.ndarray

Encodes the awi as an array

extra_obs(awi: scml.oneshot.awi.OneShotAWI) → list[tuple[float, int] | float]

The observation values other than offers and previous offers.

Returns:

A list of tuples. Each is some observation variable as a real number between zero and one and a number of bins to use for discrediting this variable. If a single value, the number of bins will be self.n_bin

get_offers(awi: scml.oneshot.awi.OneShotAWI, encoded: numpy.ndarray) → dict[str, negmas.outcomes.Outcome | None]

Gets offers from an encoded awi.

scml.std.DefaultObservationManager

The default observation manager

scml.std.random_action(obs: numpy.ndarray, env: scml.oneshot.rl.env.OneShotEnv) → numpy.ndarray

Samples a random action from the action space of the

scml.std.random_policy(obs: numpy.ndarray, env: scml.oneshot.rl.env.OneShotEnv, pend: float = 0.05, paccept: float = 0.15) → numpy.ndarray

Ends the negotiation or accepts with a predefined probability or samples a random response.

scml.std.greedy_policy(obs: numpy.ndarray, awi: scml.oneshot.awi.OneShotAWI, obs_manager: scml.oneshot.rl.observation.ObservationManager, action_manager: scml.oneshot.rl.action.ActionManager = FlexibleActionManager(ANACOneShotContext()), debug=False, distributor: Callable[[int, int], list[int]] = all_but_concentrated) → numpy.ndarray

A simple greedy policy.

Parameters:

• obs – The current observation

• awi – The AWI of the agent running the policy

• obs_manager – The observation manager used to encode the observation

• action_manager – The action manager to be used to encode the action

• debug – If True, extra assertions are tested

• distributor – A callable that receives a total quantity to be distributed over n partners and returns a list of n values that sum to this total quantity

Remarks:

• Accepts the subset of offers with maximum total quantity under current needs.

• The remaining quantity is distributed over the remaining partners using the distributor function

• Prices are set to the worst for the agent if the price range is small else they are set randomly

class scml.std.RewardFunction

Bases: Protocol

Represents a reward function.

Remarks:

• before_action is called before the action is executed for initialization and should return info to be passed to the call

• __call__ is called with the awi (to get the state), action and info and should return the reward

before_action(awi: scml.oneshot.awi.OneShotAWI) → Any

Called before executing the action from the RL agent to save any required information for calculating the reward in its return

Remarks:

The returned value will be passed as info to __call__() when it is time to calculate the reward.

__call__(awi: scml.oneshot.awi.OneShotAWI, action: dict[str, negmas.SAOResponse], info: Any) → float

Called to calculate the reward to be given to the agent at the end of a step.

Parameters:

• awi – OneShotAWI to access the agent’s state

• action – The action (decoded) as a mapping from partner ID to responses to their last offer.

• info – Information generated from before_action(). You an use this to store baselines for calculating the reward

Returns:

The reward (a number) to be given to the agent at the end of the step.

class scml.std.DefaultRewardFunction

Bases: RewardFunction

The default reward function of SCML

Remarks:

• The reward is the difference between the balance before the action and after it.

before_action(awi: scml.oneshot.awi.OneShotAWI) → float

Called before executing the action from the RL agent to save any required information for calculating the reward in its return

Remarks:

The returned value will be passed as info to __call__() when it is time to calculate the reward.

__call__(awi: scml.oneshot.awi.OneShotAWI, action: dict[str, negmas.SAOResponse], info: float)

Called to calculate the reward to be given to the agent at the end of a step.

Parameters:

• awi – OneShotAWI to access the agent’s state

• action – The action (decoded) as a mapping from partner ID to responses to their last offer.

• info – Information generated from before_action(). You an use this to store baselines for calculating the reward

Returns:

The reward (a number) to be given to the agent at the end of the step.

class scml.std.DefaultStdAdapter(*args, **kwargs)

Bases: scml.oneshot.sysagents.DefaultOneShotAdapter

The base class of all agents running in Std based on StdAgent.

Remarks:

• It inherits from Adapter allowing it to just pass any calls not defined explicity in it to the internal _obj object representing the StdAgent.

class scml.std._StdSystemAgent(*args, role, **kwargs)

Bases: DefaultOneShotAdapter

Implements an agent for handling system operations

property type_name

Returns a short name of the type of this entity

property short_type_name

Returns a short name of the type of this entity

respond_to_negotiation_request(initiator: str, issues: list[negmas.Issue], annotation: dict[str, Any], mechanism: negmas.NegotiatorMechanismInterface) → negmas.Negotiator | None

before_step()

step()

Override this method to modify stepping logic

init()

Override this method to modify initialization logic

on_negotiation_failure(partners: list[str], annotation: dict[str, Any], mechanism: negmas.NegotiatorMechanismInterface, state: negmas.MechanismState) → None

Called whenever a negotiation ends without agreement

on_negotiation_success(contract: negmas.Contract, mechanism: negmas.NegotiatorMechanismInterface) → None

Called whenever a negotiation ends with agreement

sign_all_contracts(contracts: list[negmas.Contract]) → list[str | None]

Signs all contracts

class scml.std.StdUFun(ex_pin: int, ex_qin: int, ex_pout: int, ex_qout: int, input_product: int, input_agent: bool, output_agent: bool, production_cost: float, disposal_cost: float, storage_cost: float, shortfall_penalty: float, input_penalty_scale: float | None, output_penalty_scale: float | None, storage_penalty_scale: float | None, n_input_negs: int, n_output_negs: int, current_step: int, agent_id: str | None, time_range: tuple[int, int], inventory_in: int = 0, inventory_out: int = 0, input_qrange: tuple[int, int] = (0, 0), input_prange: tuple[int, int] = (0, 0), output_qrange: tuple[int, int] = (0, 0), output_prange: tuple[int, int] = (0, 0), force_exogenous: bool = True, n_lines: int = 10, normalized: bool = False, current_balance: int | float = float('inf'), suppliers: set[str] = set(), consumers: set[str] = set(), perishable=True, **kwargs)

Bases: scml.oneshot.ufun.OneShotUFun

Calculates the utility function of a list of contracts or offers.

Parameters:

• force_exogenous – Is the agent forced to accept exogenous contracts given through ex_* arguments?

• ex_pin – total price of exogenous inputs for this agent

• ex_qin – total quantity of exogenous inputs for this agent

• ex_pout – total price of exogenous outputs for this agent

• ex_qout – total quantity of exogenous outputs for this agent.

• cost – production cost of the agent.

• disposal_cost – disposal cost per unit of input/output.

• shortfall_penalty – penalty for failure to deliver one unit of output.

• input_agent – Is the agent an input agent which means that its input product is the raw material

• output_agent – Is the agent an output agent which means that its output product is the final product

• n_lines – Number of production lines. If None, will be read through the AWI.

• input_product – Index of the input product. If None, will be read through the AWI

• input_qrange – A 2-int tuple giving the range of input quantities negotiated. If not given will be read through the AWI

• input_prange – A 2-int tuple giving the range of input unit prices negotiated. If not given will be read through the AWI

• output_qrange – A 2-int tuple giving the range of output quantities negotiated. If not given will be read through the AWI

• output_prange – A 2-int tuple giving the range of output unit prices negotiated. If not given will be read through the AWI

• n_input_negs – How many input negotiations are allowed. If not given, it will be the number of suppliers as given by the AWI

• n_output_negs – How many output negotiations are allowed. If not given, it will be the number of consumers as given by the AWI

• current_step – Current simulation step. Needed only for ufun_range when returning best outcomes

• normalized – If given the values returned by from_*, utility_range and __call__ will all be normalized between zero and one.

Remarks:

• The utility function assumes that the agent will have to pay for all its input products but will receive money only for the output products it could generate and sell.

• The utility function respects production capacity (n. lines). The agent cannot produce more than the number of lines it has.

• disposal cost is paid for items bought but not produced only. Items consumed in production (i.e. sold) are not counted.

scml.std.UFunLimit

Information about one utility limit (either highest or lowest). See OnShotUFun.find_limit for details.

class scml.std.UtilityInfo

producible: int

total_input: int

total_output: int

shortfall_quantity: int

shortfall_penalty: float

remaining_quantity: int

disposal_cost: float

storage_cost: float

utility: float

class scml.std.StdWorld(*args, horizon=STD_DEFAULT_PARAMS['horizon'], price_range_fraction=STD_DEFAULT_PARAMS['price_range_fraction'], price_multiplier=STD_DEFAULT_PARAMS['price_multiplier'], wide_price_range=STD_DEFAULT_PARAMS['wide_price_range'], one_time_per_negotiation=STD_DEFAULT_PARAMS['one_time_per_negotiation'], perishable=STD_DEFAULT_PARAMS['perishable'], quantity_multiplier=STD_DEFAULT_PARAMS['quantity_multiplier'], **kwargs)

Bases: scml.oneshot.world.SCMLBaseWorld

The world representing the base standard simulation (starting SCML 2024)

classmethod generate(*args, n_processes=STD_DEFAULT_PARAMS['n_processes'], disposal_cost=STD_DEFAULT_PARAMS['disposal_cost'], disposal_cost_dev=STD_DEFAULT_PARAMS['disposal_cost_dev'], storage_cost=STD_DEFAULT_PARAMS['storage_cost'], storage_cost_dev=STD_DEFAULT_PARAMS['storage_cost_dev'], perishable=STD_DEFAULT_PARAMS['perishable'], max_productivity=STD_DEFAULT_PARAMS['max_productivity'], max_supply=STD_DEFAULT_PARAMS['max_supply'], exogenous_supply_predictability=STD_DEFAULT_PARAMS['exogenous_supply_predictability'], exogenous_sales_predictability=STD_DEFAULT_PARAMS['exogenous_sales_predictability'], cap_exogenous_quantities=STD_DEFAULT_PARAMS['cap_exogenous_quantities'], **kwargs) → dict[str, Any]

Generates the configuration for a world

Remarks:

• This method just sets the defaults differently to create a std instead of a oneshot world.

class scml.std.SCML2024StdWorld(*args, horizon=STD_DEFAULT_PARAMS['horizon'], price_range_fraction=STD_DEFAULT_PARAMS['price_range_fraction'], price_multiplier=STD_DEFAULT_PARAMS['price_multiplier'], wide_price_range=STD_DEFAULT_PARAMS['wide_price_range'], one_time_per_negotiation=STD_DEFAULT_PARAMS['one_time_per_negotiation'], perishable=STD_DEFAULT_PARAMS['perishable'], quantity_multiplier=STD_DEFAULT_PARAMS['quantity_multiplier'], **kwargs)

Bases: StdWorld

The SCML-standard simulation as used in [SCML 2024](https://scml.cs.brown.edu)

scml.std.STD_DEFAULT_PARAMS

scml.std.STD_DEFAULT_PARAMS2024

scml.std.PLACEHOLDER_AGENT_PREFIX = 'PlaceHolder__'

scml.std.builtin_agent_types(as_str=False)

Returns all built-in agents.

Parameters:

as_str – If true, the full type name will be returned otherwise the type object itself.

scml.std.__all__