Documentation for make_env

src.pcgym.pcgym.make_env

Bases: Env

__init__(env_params)

Initialize the environment with given parameters.

Parameters:

Name	Type	Description	Default
env_params	dict	Environment configuration parameters including model selection, spaces, simulation parameters, constraints, and custom functions.	required

reset(seed=0, **kwargs)

Reset the environment to its initial state.

This method resets the environment's state, time, and other relevant variables. It's called at the beginning of each episode.

Parameters:

Name	Type	Description	Default
seed	int	Seed for random number generator.	0
**kwargs		Additional keyword arguments.	{}

Returns:

Name	Type	Description
tuple	tuple[array, dict]	A tuple containing: - numpy.array: The initial state observation. - dict: Additional information (e.g., initial reward).

step(action)

Perform one time step in the environment.

This method takes an action, applies it to the environment, and returns the next state, reward, and other information.

Parameters:

Name	Type	Description	Default
action	array	The action to be taken in the environment.	required

Returns:

Name	Type	Description
tuple	tuple[array, float, bool, bool, dict]	A tuple containing: - numpy.array: The next state observation. - float: The reward for the current step. - bool: Whether the episode has ended. - bool: Whether the episode was truncated. - dict: Additional information about the step.

con_checker(curr_state, inputs)

Check if any constraints are violated for the given states.

Parameters:

Name	Type	Description	Default
model_states	list	List of state or input names to check.	required
curr_state	list	List of corresponding state or input values.	required

Returns:

Name	Type	Description
bool	bool	True if any constraint is violated, False otherwise.

constraint_check(state, input)

Check if any constraints are violated in the current step.

This method checks both state and input constraints, as well as any custom constraints defined by the user.

Parameters:

Name	Type	Description	Default
state	array	The current state of the system.	required
input	array	The current input (action) applied to the system.	required

Returns:

Name	Type	Description
bool	bool	True if any constraint is violated, False otherwise.

get_rollouts(policies, reps, oracle=False, dist_reward=False, MPC_params=False, cons_viol=False)

Generate rollouts for the given policies.

This method simulates the environment for multiple episodes using the provided policies.

Parameters:

Name	Type	Description	Default
policies	dict	Dictionary of policies to evaluate.	required
reps	int	Number of rollouts to perform.	required
oracle	bool	Whether to use an oracle model for evaluation. Defaults to False.	False
dist_reward	bool	Whether to use reward distribution. Defaults to False.	False
MPC_params	bool	Whether to use MPC parameters. Defaults to False.	False
cons_viol	bool	Whether to track constraint violations. Defaults to False.	False

Returns:

Name	Type	Description
tuple	tuple[policy_eval, dict]	A tuple containing: - policy_eval: The policy evaluator object. - dict: Data from the rollouts.

plot_rollout(policies, reps, oracle=False, dist_reward=False, MPC_params=False, cons_viol=False, save_fig=False)

Generate and plot rollouts for the given policies.

This method simulates the environment for multiple episodes using the provided policies and plots the results.

Parameters:

Name	Type	Description	Default
policies	dict	Dictionary of policies to evaluate.	required
reps	int	Number of rollouts to perform.	required
oracle	bool	Whether to use an oracle model for evaluation. Defaults to False.	False
dist_reward	bool	Whether to use reward distribution for plotting. Defaults to False.	False
MPC_params	bool	Whether to use MPC parameters. Defaults to False.	False
cons_viol	bool	Whether to track constraint violations. Defaults to False.	False
save_fig	bool	Whether to save the generated figures. Defaults to False.	False

Returns:

Name	Type	Description
tuple	tuple[policy_eval, dict]	A tuple containing: - policy_eval: The policy evaluator object. - dict: Data from the rollouts.