mirror of
https://github.com/vale981/ray
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93 lines
3.1 KiB
Python
93 lines
3.1 KiB
Python
from typing import Any, Dict, List, Tuple, Union
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import gym
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# Represents a fully filled out config of a Trainer class.
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# Note: Policy config dicts are usually the same as TrainerConfigDict, but
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# parts of it may sometimes be altered in e.g. a multi-agent setup,
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# where we have >1 Policies in the same Trainer.
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TrainerConfigDict = dict
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# A trainer config dict that only has overrides. It needs to be combined with
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# the default trainer config to be used.
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PartialTrainerConfigDict = dict
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# Represents the env_config sub-dict of the trainer config that is passed to
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# the env constructor.
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EnvConfigDict = dict
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# Represents the model config sub-dict of the trainer config that is passed to
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# the model catalog.
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ModelConfigDict = dict
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# Represents a BaseEnv, MultiAgentEnv, ExternalEnv, ExternalMultiAgentEnv,
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# VectorEnv, or gym.Env.
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EnvType = Any
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# Represents a generic identifier for an agent (e.g., "agent1").
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AgentID = Any
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# Represents a generic identifier for a policy (e.g., "pol1").
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PolicyID = str
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# Type of the config["multiagent"]["policies"] dict for multi-agent training.
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MultiAgentPolicyConfigDict = Dict[PolicyID, Tuple[type, gym.Space, gym.Space,
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PartialTrainerConfigDict]]
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# Represents an environment id.
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EnvID = int
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# Represents an episode id.
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EpisodeID = int
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# A dict keyed by agent ids, e.g. {"agent-1": value}.
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MultiAgentDict = Dict[AgentID, Any]
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# A dict keyed by env ids that contain further nested dictionaries keyed by
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# agent ids. e.g., {"env-1": {"agent-1": value}}.
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MultiEnvDict = Dict[EnvID, MultiAgentDict]
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# Represents an observation returned from the env.
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EnvObsType = Any
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# Represents an action passed to the env.
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EnvActionType = Any
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# Info dictionary returned by calling step() on gym envs. Commonly empty dict.
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EnvInfoDict = dict
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# Represents a File object
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FileType = Any
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# Represents the result dict returned by Trainer.train().
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ResultDict = dict
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# Dict of tensors returned by compute gradients on the policy, e.g.,
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# {"td_error": [...], "learner_stats": {"vf_loss": ..., ...}}, for multi-agent,
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# {"policy1": {"learner_stats": ..., }, "policy2": ...}.
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GradInfoDict = dict
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# Dict of learner stats returned by compute gradients on the policy, e.g.,
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# {"vf_loss": ..., ...}. This will always be nested under the "learner_stats"
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# key(s) of a GradInfoDict. In the multi-agent case, this will be keyed by
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# policy id.
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LearnerStatsDict = dict
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# Represents a generic tensor type.
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# This could be an np.ndarray, tf.Tensor, or a torch.Tensor.
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TensorType = Any
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# List of grads+var tuples (tf) or list of gradient tensors (torch)
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# representing model gradients and returned by compute_gradients().
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ModelGradients = Union[List[Tuple[TensorType, TensorType]], List[TensorType]]
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# Type of dict returned by get_weights() representing model weights.
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ModelWeights = dict
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# Some kind of sample batch.
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SampleBatchType = Union["SampleBatch", "MultiAgentBatch"]
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# Either a plain tensor, or a dict or tuple of tensors (or StructTensors).
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TensorStructType = Union[TensorType, dict, tuple]
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# A shape of a tensor.
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TensorShape = Union[Tuple[int], List[int]]
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