smartgrid.rewards.numeric.per_agent.adaptability.AdaptabilityTwoPerAgent

class smartgrid.rewards.numeric.per_agent.adaptability.AdaptabilityTwoPerAgent

Bases: Reward

Equity when t<2000, (Equity+OverConsumption)/2 otherwise.

This reward function changes its definition after time step t=2000. With t < 2000, it performs exactly as the EquityPerAgent reward function. When t >= 2000, it returns the average of EquityPerAgent and the OverConsumptionPerAgent reward functions.

Thus, the targeted objectives increase in the second phase: the initial one is kept, and a new one is added (equity vs equity+overconsumption). This makes this reward function useful to evaluate whether agents are able to change their behaviour by taking into account new objectives in addition to previous ones.

This reward function is easier than AdaptabilityOnePerAgent (which completely replace the set of objectives) and AdaptabilityThreePerAgent (which uses 3 phases instead of 2).

__init__()

Methods

__init__()
calculate(world, agent)	Compute the reward for a specific Agent at the current time step.
is_activated(world, agent)	Determines whether the reward function should produce a reward.
reset()	Reset the reward function.

Attributes

name	Uniquely identifying, human-readable name for this reward function.

calculate(world, agent) → float

Compute the reward for a specific Agent at the current time step.

Parameters:

• world – The World, used to get the current state and determine consequences of the agent’s action.

• agent – The Agent that is rewarded, used to access particular information about the agent (personal state) and its action.

Returns:

A reward, i.e., a single value describing how well the agent performed. The higher the reward, the better its action was. Typically, a value in [0,1] but any range can be used.

is_activated(world: World, agent: Agent) → bool

Determines whether the reward function should produce a reward.

This function can be used to enable/disable the reward function at will, allowing for a variety of use cases (changing the reward function over the time, using different reward functions for different agents, etc.).

By default, it returns True to avoid forcing the definition of this function. To specify when this reward function should be activated, two ways are possible:

• Wrap the Reward object in a constraint class, e.g., TimeConstrainedReward.

• Override this method in the subclass to implement the desired activation mechanism.

Parameters:

• world – The World in which the reward function may be activated.

• agent – The Agent that should (potentially) be rewarded by this reward function.

Returns:

A boolean indicating whether the reward function should produce a reward at this moment (for this state of the world and this learning agent).

name: str

Uniquely identifying, human-readable name for this reward function.

reset()

Reset the reward function.

This function must be overridden by reward functions that use a state, so that the state is reset with the environment. By default, does nothing, as most reward functions do not use a state.