# Environment¶

This module defines the base class for the environments.

class deer.base_classes.Environment

All your Environment classes should inherit this interface.

The environment defines the dynamics and the reward signal that the agent observes when interacting with it.

An agent sees at any time-step from the environment a collection of observable elements. Observing the environment at time t thus corresponds to obtaining a punctual observation for each of these elements. According to the control problem to solve, it might be useful for the agent to not only take action based on the current punctual observations but rather on a collection of the last punctual observations. In this framework, it’s the environment that defines the number of each punctual observation to be considered.

Different “modes” are used in this framework to allow the environment to have different dynamics and/or reward signal. For instance, in training mode, only a part of the dynamics may be available so that it is possible to see how well the agent generalizes to a slightly different one.

act(action)

Applies the agent action [action] on the environment.

action : int
The action selected by the agent to operate on the environment. Should be an identifier included between 0 included and nActions() excluded.
end()

Optional hook called at the end of all epochs

inTerminalState()

Tells whether the environment reached a terminal state after the last transition (i.e. the last transition that occured was terminal).

As the majority of control tasks considered have no end (a continuous control should be operated), by default this returns always False. But in the context of a video game for instance, terminal states can happen and in these cases, this method should be overridden.

isTerminal : bool
Whether or not the current state is terminal
inputDimensions()

Gets the shape of the input space for this environment.

This returns a list whose length is the number of observations in the environment. Each element of the list is a tuple: the first integer is always the history size considered for this observation and the rest describes the shape of the observation at a given time step. For instance: - () or (1,) means each observation at a given time step is a single scalar and the history size is 1 (= only current observation) - (N,) means each observation at a given time step is a single scalar and the history size is N - (N, M) means each observation at a given time step is a vector of length M and the history size is N - (N, M1, M2) means each observation at a given time step is a 2D matrix with M1 rows and M2 columns and the history size is N

nActions()

Gets the number of different actions that can be taken on this environment. It can be either an integer in the case of a finite discrete number of actions or it can be a list of couples [min_action_value,max_action_value] for a continuous action space

observationType(subject)

Gets the most inner type (np.uint8, np.float32, …) of [subject].

subject : int
The subject
observe()

Gets a list of punctual observations composing this environment.

This returns a list where element i is a punctual observation. Note that the history of observations is not returned and only the current observation is.

reset(mode)

Resets the environment and put it in mode [mode]. This function is called when beginning every new episode.

The [mode] can be used to discriminate for instance between an agent which is training or trying to get a validation or generalization score. The mode the environment is in should always be redefined by resetting the environment using this method, meaning that the mode should be preserved until the next call to reset().

mode : int
The mode to put the environment into. Mode “-1” is reserved and always means “training”.

Initialization of the pseudo state at the beginning of a new episode: list (of lists) with size given by inputDimensions

summarizePerformance(test_data_set, *args, **kwargs)

Optional hook that can be used to show a summary of the performance of the agent on the environment in the current mode.

test_data_set : agent.DataSet
The dataset maintained by the agent in the current mode, which contains observations, actions taken and rewards obtained, as well as wether each transition was terminal or not. Refer to the documentation of agent.DataSet for more information.