ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2#

class ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2(observation_space: gymnasium.spaces.Space, action_space: gymnasium.spaces.Space, config: dict, **kwargs)[source]#

Bases: Policy

A TF-eager / TF2 based tensorflow policy.

This class is intended to be used and extended by sub-classing.

Methods

`action_distribution_fn`	Action distribution function for this Policy.
`action_sampler_fn`	Custom function for sampling new actions given policy.
`apply`	Calls the given function with this Policy instance.
`apply_gradients_fn`	Gradients computing function (from loss tensor, using local optimizer).
`compute_gradients_fn`	Gradients computing function (from loss tensor, using local optimizer).
`compute_single_action`	Computes and returns a single (B=1) action value.
`export_checkpoint`	Exports Policy checkpoint to a local directory and returns an AIR Checkpoint.
`extra_action_out_fn`	Extra values to fetch and return from compute_actions().
`extra_learn_fetches_fn`	Extra stats to be reported after gradient computation.
`from_checkpoint`	Creates new Policy instance(s) from a given Policy or Algorithm checkpoint.
`from_state`	Recovers a Policy from a state object.
`get_batch_divisibility_req`	Get batch divisibility request.
`get_connector_metrics`	Get metrics on timing from connectors.
`get_host`	Returns the computer's network name.
`get_num_samples_loaded_into_buffer`	Returns the number of currently loaded samples in the given buffer.
`get_session`	Returns tf.Session object to use for computing actions or None.
`grad_stats_fn`	Gradient stats function.
`import_model_from_h5`	Imports Policy from local file.
`init_view_requirements`	Maximal view requirements dict for `learn_on_batch()` and `compute_actions` calls.
`learn_on_batch_from_replay_buffer`	Samples a batch from given replay actor and performs an update.
`learn_on_loaded_batch`	Runs a single step of SGD on an already loaded data in a buffer.
`load_batch_into_buffer`	Bulk-loads the given SampleBatch into the devices' memories.
`loss`	Compute loss for this policy using model, dist_class and a train_batch.
`make_model`	Build underlying model for this Policy.
`make_rl_module`	Returns the RL Module (only for when RLModule API is enabled.)
`maybe_add_time_dimension`	Adds a time dimension for recurrent RLModules.
`on_global_var_update`	Called on an update to global vars.
`optimizer`	TF optimizer to use for policy optimization.
`postprocess_trajectory`	Post process trajectory in the format of a SampleBatch.
`reset_connectors`	Reset action- and agent-connectors for this policy.
`restore_connectors`	Restore agent and action connectors if configs available.
`stats_fn`	Stats function.
`variables`	Return the list of all savable variables for this policy.