schola.core.env.ScholaEnv

class schola.core.env.ScholaEnv(unreal_connection, verbosity=0, environment_start_timeout=45) : Bases: object

A Gym-Like Environment that wraps a connection to the Unreal Engine, running the Schola Plugin for Unreal.

Parameters: : - unreal_connection (UnrealConnection) – The connection to the Unreal Engine.

• verbosity (int, default=0) – The verbosity level for the environment.
• environment_start_timeout (int, default=45) – The time to wait for the environment to start in seconds.

unreal_connection : The connection to the Unreal Engine.

Type: : UnrealConnection

gym_stub : The gRPC stub for the Gym Service.

Type: : gym_grpc.GymServiceStub

ids : A nested list of all the environments and their active agents.

Type: : List[List[int]]

agent_display_names : A list of mappings from the id to the display names for each agent in each environment.

Type: : List[Dict[int,str]]

obs_defns : The observation space definitions for each agent in each environment.

Type: : Dict[int,Dict[int,DictSpace]]

action_defns : The action space definitions for each agent in each environment.

Type: : Dict[int,Dict[int,DictSpace]]

steps : The number of steps taken in the current episode of the environment.

Type: : int

next_action : The next action to be taken by each agent in each environment.

Type: : Dict[int,Dict[int,Any]], optional

Raises: : - NoEnvironmentsException – If there are no environment definitions.

• NoAgentsException – If there are no agents defined for any environment.

Parameters: : - unreal_connection (UnrealConnection)

• verbosity (int)
• environment_start_timeout (int)

Methods

__init__(unreal_connection[, verbosity, …])
close()	Closes the connection to the Unreal Engine and cleans up any resources.
get_action_space(env_id, agent_id)	Get the action space for a specific environment and agent.
get_obs_space(env_id, agent_id)	Get the observation space for a specific environment and agent.
hard_reset([env_ids, seeds, options])	Perform a hard reset on the environment.
poll()	Polls the environment for the current state.
send_actions(action)	Send Actions to all agents and environments.
soft_reset([ids])	Soft reset the environment, by waiting for Unreal to rself reset and send a Post Reset State to python.

Attributes

num_agents	Return the total number of agents in the environment.
num_envs	Return the number of environments.

__init__(unreal_connection, verbosity=0, environment_start_timeout=45) : Parameters: : - unreal_connection (UnrealConnection)

• verbosity (int)
• environment_start_timeout (int)

close() : Closes the connection to the Unreal Engine and cleans up any resources. It is safe to call this method multiple times.

See also

gymnasium.Env.close : The equivalent operation in gymnasium

Return type: : None

get_action_space(env_id, agent_id) : Get the action space for a specific environment and agent.

Parameters: : - env_id (int) – The ID of the environment.

• agent_id (int) – The ID of the agent.

Returns: : The action space for the specified environment and agent.

Return type: : DictSpace

get_obs_space(env_id, agent_id) : Get the observation space for a specific environment and agent.

Parameters: : - env_id (int) – The ID of the environment.

• agent_id (int) – The ID of the agent.

Returns: : The observation space for the specified environment and agent.

Return type: : DictSpace

hard_reset(env_ids=None, seeds=None, options=None) : Perform a hard reset on the environment.

Parameters: : - env_ids (Optional*[List[int]**]*) – A list of environment IDs to reset. If None, all environments will be reset. Default is None.

• seeds (Union*[None,* List*[int]**,* int]) – The seeds to use for random number generation. If an int is provided, it will be used as the seed for all environments. If a list of ints is provided, each environment will be assigned a seed from the list. Default is None.
• options (Union*[List[Dict[str,str]],* Dict*[str,str]**,* None*]*) – The options to set for each environment. If a list of dictionaries is provided, each environment will be assigned the corresponding dictionary of options. If a single dictionary is provided, all environments will be assigned the same options. Default is None.

Returns: : A list of environment IDs that were reset.

Return type: : List

Raises: : - AssertionError – If the number of seeds provided, is not zero or one, and does not match the number of environments.

• AssertionError – If the number of options dictionaries provided, is not zero or one, does not match the number of environments.

Notes

• If seeds are provided, the environment will be seeded with the specified values.
• If options are provided, the environment will be configured with the specified options.

See also

gymnasium.Env.reset : The equivalent operation in gymnasium

property num_agents*: int* : Return the total number of agents in the environment.

Returns: : The total number of agents.

Return type: : int

property num_envs*: int* : Return the number of environments. :returns: The number of environments. :rtype: int

poll() : Polls the environment for the current state.

Returns: : - observations (EnvAgentIdDict[Dict[str,Any]]) – A dictionary, keyed by the environment and agent Id, containing the observations for each agent.

• rewards (EnvAgentIdDict[float]) – A dictionary, keyed by the environment and agent Id, containing the reward for each agent.
• terminateds (EnvAgentIdDict[bool]) – A dictionary, keyed by the environment and agent Id, containing the termination flag for each agent.
• truncateds (EnvAgentIdDict[bool]) – A dictionary, keyed by the environment and agent Id, containing the truncation flag for each agent.
• infos (EnvAgentIdDict[Dict[str,str]]]:) – A dictionary, keyed by the environment and agent Id, containing the information dictionary for each agent.

Return type: : Tuple[Dict[int, Dict[int, Dict[str, Any]]], Dict[int, Dict[int, float]], Dict[int, Dict[int, bool]], Dict[int, Dict[int, bool]], Dict[int, Dict[int, Dict[str, str]]]]

send_actions(action) : Send Actions to all agents and environments.

Parameters: : action (EnvAgentIdDict*[Dict[str,Any]**]*) – A dictionary, keyed by the environment and agent Id, containing the actions for all active environments and agents.

Return type: : None

Notes

The actions are not sent to Unreal until Poll is called.

See also

poll : Where the actions are actually sent to unreal

soft_reset(ids=None) : Soft reset the environment, by waiting for Unreal to rself reset and send a Post Reset State to python.

Parameters: : ids (List*[str]**,* optional) – A list of environment IDs to reset. If not provided or set to None, all environment IDs will be reset.

Returns: : - observations (EnvAgentIdDict[Dict[str,Any]]) – A dictionary, keyed by the environment and agent Id, containing the observations of the agents in the environments immediately following a reset

• infos (EnvAgentIdDict[Dict[str,str]]) – A dictionary, keyed by the environment and agent Id, containing the infos of the agents in the environment

Return type: : Tuple[Dict[int, Dict[int, Dict[str, Any]]], Dict[int, Dict[int, Dict[str, str]]]]