smartgrid.rewards.numeric.differentiated.multi_objective_sum.MultiObjectiveSum

class smartgrid.rewards.numeric.differentiated.multi_objective_sum.MultiObjectiveSum(coefficients=None)

Bases: Reward

Weighted sum of multiple objectives: comfort, and over-consumption.

The reward is equal to 0.2 * comfort + 0.8 * overconsumption, where comfort refers to the reward of Comfort, and overconsumption refers to the reward of OverConsumption.

The coefficients (0.2 and 0.8) can be configured in the constructor. Note that, in this case, the sum of coefficients should be equal to 1, in order to have a weighted average, but this is not strictly mandatory.

__init__(coefficients=None)

Methods

__init__([coefficients])
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)

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.