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batched_py_environment module: Treat multiple non-batch environments as a single batch environment.
gym_wrapper module: Wrapper providing a PyEnvironmentBase adapter for Gym environments.
parallel_py_environment module: Runs multiple environments in parallel processes and steps them in batch.
py_environment module: Python RL Environment API.
random_py_environment module: Environment implementation that generates random observations.
random_tf_environment module: Utility environment that creates random observations.
suite_gym module: Suite for loading Gym Environments.
tf_environment module: TensorFlow RL Environment API.
tf_py_environment module: Wrapper for PyEnvironments into TFEnvironments.
trajectory_replay module: A Driver-like object that replays Trajectories.
utils module: Common utilities for TF-Agents Environments.
wrappers module: Environment wrappers.
class ActionClipWrapper: Wraps an environment and clips actions to spec before applying.
class ActionDiscretizeWrapper: Wraps an environment with continuous actions and discretizes them.
class ActionOffsetWrapper: Offsets actions to be zero-based.
class ActionRepeat: Repeates actions over n-steps while acummulating the received reward.
class BatchedPyEnvironment: Batch together multiple py environments and act as a single batch.
class FlattenObservationsWrapper: Wraps an environment and flattens nested multi-dimensional observations.
class GoalReplayEnvWrapper: Adds a goal to the observation, used for HER (Hindsight Experience Replay).
class HistoryWrapper: Adds observation and action history to the environment's observations.
class ObservationFilterWrapper: Filters observations based on an array of indexes.
class OneHotActionWrapper: Converts discrete action to one_hot format.
class ParallelPyEnvironment: Batch together environments and simulate them in external processes.
class PerformanceProfiler: End episodes after specified number of steps.
class PyEnvironment: Abstract base class for Python RL environments.
class PyEnvironmentBaseWrapper: PyEnvironment wrapper forwards calls to the given environment.
class RandomPyEnvironment: Randomly generates observations following the given observation_spec.
class RandomTFEnvironment: Randomly generates observations following the given observation_spec.
class RunStats: Wrapper that accumulates run statistics as the environment iterates.
class TFEnvironment: Abstract base class for TF RL environments.
class TFPyEnvironment: Exposes a Python environment as an in-graph TF environment.
class TimeLimit: End episodes after specified number of steps.
class TrajectoryReplay: A helper that replays a policy against given
validate_py_environment(...): Validates the environment follows the defined specs.