vmas_envs#
Vectorized Multi-Agent Simulator (VMAS) Environment Wrapper
Classes#
Vectorized Multi-Agent Simulator (VMAS) Multi-Group Environment Wrapper
Vectorized Multi-Agent Simulator (VMAS)
- class prt_rl.env.wrappers.vmas_envs.VmasMultiGroupWrapper(scenario: str, render_mode: str | None = None, **kwargs)[source]#
Vectorized Multi-Agent Simulator (VMAS) Multi-Group Environment Wrapper
The VMAS Multi-Group wrapper provides an interface to VMAS multi-agent environments where agents belong to multiple groups. This wrapper implements the MultiGroupEnvironmentInterface.
Examples
- Parameters:
References
[1] proroklab/VectorizedMultiAgentSimulator
- get_num_envs() int#
Returns the number of environments in the interface.
- Returns:
Number of environments
- Return type:
- get_parameters() MultiGroupEnvParams[source]#
Returns the EnvParams object which contains information about the sizes of observations and actions needed for setting up RL agents.
- Returns:
environment parameters object
- Return type:
- reset(seed: int | None = None) Tuple[Dict[str, Tensor], Dict[str, Any]][source]#
Resets the environment to the initial state and returns the initial observation.
- Parameters:
seed (int | None) – Sets the random seed.
- Returns:
Tuple of tensors containing the initial observation and info dictionary
- Return type:
Tuple
- step(action: Dict[str, Tensor]) Tuple[Tensor, Tensor, Tensor, Dict[str, Any]][source]#
Steps the simulation using the action tensor and returns the new trajectory.
- Parameters:
action (torch.Tensor) – Tensor with “action” key that is a tensor with shape (# env, # agents, # actions)
- Returns:
Tuple of tensors containing the next state, reward, done, and info dictionary
- Return type:
Tuple
- class prt_rl.env.wrappers.vmas_envs.VmasWrapper(scenario: str, render_mode: str | None = None, **kwargs)[source]#
Vectorized Multi-Agent Simulator (VMAS)
The VMAS wrapper provides an interface to VMAS multi-agent environments where all agents belong to a single group. VmasMultiGroupWrapper should be used for environments with multiple agent groups.
Examples
- Parameters:
References
[1] proroklab/VectorizedMultiAgentSimulator
- get_num_envs() int#
Returns the number of environments in the interface.
- Returns:
Number of environments
- Return type:
- get_parameters() MultiAgentEnvParams[source]#
Returns the EnvParams object which contains information about the sizes of observations and actions needed for setting up RL agents.
- Returns:
environment parameters object
- Return type:
- reset(seed: int | None = None) Tuple[Tensor, Dict[str, Any]][source]#
Resets the environment to the initial state and returns the initial observation.
- Parameters:
seed (int | None) – Sets the random seed.
- Returns:
Tuple of tensors containing the initial observation and info dictionary
- Return type:
Tuple
- step(action: Tensor) Tuple[Tensor, Tensor, Tensor, Dict[str, Any]][source]#
Steps the simulation using the action tensor and returns the new trajectory.
- Parameters:
action (torch.Tensor) – Tensor with “action” key that is a tensor with shape (# env, # agents, # actions)
- Returns:
Tuple of tensors containing the next state, reward, done, and info dictionary
- Return type:
Tuple