openrl.envs.mpe package¶
Subpackages¶
Submodules¶
openrl.envs.mpe.core module¶
- class openrl.envs.mpe.core.Wall(orient='H', axis_pos=0.0, endpoints=(- 1, 1), width=0.1, hard=True)[源代码]¶
基类：
object
openrl.envs.mpe.mpe_env module¶
openrl.envs.mpe.multi_discrete module¶
- class openrl.envs.mpe.multi_discrete.MultiDiscrete(array_of_param_array)[源代码]¶
基类：
Generic[gym.spaces.space.T_cov]The multi-discrete action space consists of a series of discrete action spaces with different parameters
It can be adapted to both a Discrete action space or a continuous (Box) action space
It is useful to represent game controllers or keyboards where each key can be represented as a discrete action space
- It is parametrized by passing an array of arrays containing [min, max] for each discrete action space
where the discrete action space can take any integers from min to max (both inclusive)
Note: A value of 0 always need to represent the NOOP action. e.g. Nintendo Game Controller - Can be conceptualized as 3 discrete action spaces:
Arrow Keys: Discrete 5 - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4] - params: min: 0, max: 4
Button A: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1
Button B: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1
- Can be initialized as
MultiDiscrete([ [0,4], [0,1], [0,1] ])
- property shape¶
Return the shape of the space as an immutable property.
openrl.envs.mpe.multiagent_env module¶
- class openrl.envs.mpe.multiagent_env.MultiAgentEnv(world, reset_callback=None, reward_callback=None, observation_callback=None, info_callback=None, done_callback=None, post_step_callback=None, shared_viewer=True, discrete_action=True, render_mode=None)[源代码]¶
基类：
Generic[gymnasium.core.ObsType,gymnasium.core.ActType]- property agent_num¶
- metadata: dict[str, Any] = {'render.modes': ['human', 'rgb_array']}¶
- render(mode='rgb_array', close=False)[源代码]¶
Compute the render frames as specified by
render_modeduring the initialization of the environment.The environment's
metadatarender modes (env.metadata["render_modes"]) should contain the possible ways to implement the render modes. In addition, list versions for most render modes is achieved through gymnasium.make which automatically applies a wrapper to collect rendered frames.- Note:
As the
render_modeis known during__init__, the objects used to render the environment state should be initialised in__init__.
By convention, if the
render_modeis:None (default): no render is computed.
"human": The environment is continuously rendered in the current display or terminal, usually for human consumption. This rendering should occur during
step()andrender()doesn't need to be called. ReturnsNone."rgb_array": Return a single frame representing the current state of the environment. A frame is a
np.ndarraywith shape(x, y, 3)representing RGB values for an x-by-y pixel image."ansi": Return a strings (
str) orStringIO.StringIOcontaining a terminal-style text representation for each time step. The text can include newlines and ANSI escape sequences (e.g. for colors)."rgb_array_list" and "ansi_list": List based version of render modes are possible (except Human) through the wrapper,
gymnasium.wrappers.RenderCollectionthat is automatically applied duringgymnasium.make(..., render_mode="rgb_array_list"). The frames collected are popped afterrender()is called orreset().
- Note:
Make sure that your class's
metadata"render_modes"key includes the list of supported modes.
在 0.25.0 版更改: The render function was changed to no longer accept parameters, rather these parameters should be specified in the environment initialised, i.e.,
gymnasium.make("CartPole-v1", render_mode="human")
- reset(*, seed: Optional[Union[int, List[int]]] = None, options: Optional[Dict[str, Any]] = None) Tuple[gymnasium.core.ObsType, Dict[str, Any]][源代码]¶
Resets the environment to an initial internal state, returning an initial observation and info.
This method generates a new starting state often with some randomness to ensure that the agent explores the state space and learns a generalised policy about the environment. This randomness can be controlled with the
seedparameter otherwise if the environment already has a random number generator andreset()is called withseed=None, the RNG is not reset.Therefore,
reset()should (in the typical use case) be called with a seed right after initialization and then never again.For Custom environments, the first line of
reset()should besuper().reset(seed=seed)which implements the seeding correctly.在 v0.25 版更改: The
return_infoparameter was removed and now info is expected to be returned.- Args:
- seed (optional int): The seed that is used to initialize the environment's PRNG (np_random).
If the environment does not already have a PRNG and
seed=None(the default option) is passed, a seed will be chosen from some source of entropy (e.g. timestamp or /dev/urandom). However, if the environment already has a PRNG andseed=Noneis passed, the PRNG will not be reset. If you pass an integer, the PRNG will be reset even if it already exists. Usually, you want to pass an integer right after the environment has been initialized and then never again. Please refer to the minimal example above to see this paradigm in action.- options (optional dict): Additional information to specify how the environment is reset (optional,
depending on the specific environment)
- Returns:
- observation (ObsType): Observation of the initial state. This will be an element of
observation_space (typically a numpy array) and is analogous to the observation returned by
step().- info (dictionary): This dictionary contains auxiliary information complementing
observation. It should be analogous to the
inforeturned bystep().
- observation (ObsType): Observation of the initial state. This will be an element of
- step(action_n)[源代码]¶
Run one timestep of the environment's dynamics using the agent actions.
When the end of an episode is reached (
terminated or truncated), it is necessary to callreset()to reset this environment's state for the next episode.在 0.26 版更改: The Step API was changed removing
donein favor ofterminatedandtruncatedto make it clearer to users when the environment had terminated or truncated which is critical for reinforcement learning bootstrapping algorithms.- Args:
action (ActType): an action provided by the agent to update the environment state.
- Returns:
- observation (ObsType): An element of the environment's
observation_spaceas the next observation due to the agent actions. An example is a numpy array containing the positions and velocities of the pole in CartPole.
reward (SupportsFloat): The reward as a result of taking the action. terminated (bool): Whether the agent reaches the terminal state (as defined under the MDP of the task)
which can be positive or negative. An example is reaching the goal state or moving into the lava from the Sutton and Barton, Gridworld. If true, the user needs to call
reset().- truncated (bool): Whether the truncation condition outside the scope of the MDP is satisfied.
Typically, this is a timelimit, but could also be used to indicate an agent physically going out of bounds. Can be used to end the episode prematurely before a terminal state is reached. If true, the user needs to call
reset().- info (dict): Contains auxiliary diagnostic information (helpful for debugging, learning, and logging).
This might, for instance, contain: metrics that describe the agent's performance state, variables that are hidden from observations, or individual reward terms that are combined to produce the total reward. In OpenAI Gym <v26, it contains "TimeLimit.truncated" to distinguish truncation and termination, however this is deprecated in favour of returning terminated and truncated variables.
- done (bool): (Deprecated) A boolean value for if the episode has ended, in which case further
step()calls will return undefined results. This was removed in OpenAI Gym v26 in favor of terminated and truncated attributes. A done signal may be emitted for different reasons: Maybe the task underlying the environment was solved successfully, a certain timelimit was exceeded, or the physics simulation has entered an invalid state.
- observation (ObsType): An element of the environment's
openrl.envs.mpe.rendering module¶
2D rendering framework