ray/rllib/agents/pg
Sven Mika 428516056a
[RLlib] SAC Torch (incl. Atari learning) (#7984)
* Policy-classes cleanup and torch/tf unification.
- Make Policy abstract.
- Add `action_dist` to call to `extra_action_out_fn` (necessary for PPO torch).
- Move some methods and vars to base Policy
  (from TFPolicy): num_state_tensors, ACTION_PROB, ACTION_LOGP and some more.

* Fix `clip_action` import from Policy (should probably be moved into utils altogether).

* - Move `is_recurrent()` and `num_state_tensors()` into TFPolicy (from DynamicTFPolicy).
- Add config to all Policy c'tor calls (as 3rd arg after obs and action spaces).

* Add `config` to c'tor call to TFPolicy.

* Add missing `config` to c'tor call to TFPolicy in marvil_policy.py.

* Fix test_rollout_worker.py::MockPolicy and BadPolicy classes (Policy base class is now abstract).

* Fix LINT errors in Policy classes.

* Implement StatefulPolicy abstract methods in test cases: test_multi_agent_env.py.

* policy.py LINT errors.

* Create a simple TestPolicy to sub-class from when testing Policies (reduces code in some test cases).

* policy.py
- Remove abstractmethod from `apply_gradients` and `compute_gradients` (these are not required iff `learn_on_batch` implemented).
- Fix docstring of `num_state_tensors`.

* Make QMIX torch Policy a child of TorchPolicy (instead of Policy).

* QMixPolicy add empty implementations of abstract Policy methods.

* Store Policy's config in self.config in base Policy c'tor.

* - Make only compute_actions in base Policy's an abstractmethod and provide pass
implementation to all other methods if not defined.
- Fix state_batches=None (most Policies don't have internal states).

* Cartpole tf learning.

* Cartpole tf AND torch learning (in ~ same ts).

* Cartpole tf AND torch learning (in ~ same ts). 2

* Cartpole tf (torch syntax-broken) learning (in ~ same ts). 3

* Cartpole tf AND torch learning (in ~ same ts). 4

* Cartpole tf AND torch learning (in ~ same ts). 5

* Cartpole tf AND torch learning (in ~ same ts). 6

* Cartpole tf AND torch learning (in ~ same ts). Pendulum tf learning.

* WIP.

* WIP.

* SAC torch learning Pendulum.

* WIP.

* SAC torch and tf learning Pendulum and Cartpole after cleanup.

* WIP.

* LINT.

* LINT.

* SAC: Move policy.target_model to policy.device as well.

* Fixes and cleanup.

* Fix data-format of tf keras Conv2d layers (broken for some tf-versions which have data_format="channels_first" as default).

* Fixes and LINT.

* Fixes and LINT.

* Fix and LINT.

* WIP.

* Test fixes and LINT.

* Fixes and LINT.

Co-authored-by: Sven Mika <sven@Svens-MacBook-Pro.local>
2020-04-15 13:25:16 +02:00
..
tests [RLlib] SAC Torch (incl. Atari learning) (#7984) 2020-04-15 13:25:16 +02:00
__init__.py [rllib] Support multi-agent training in pipeline impls, add easy flag to enable (#7338) 2020-03-02 15:16:37 -08:00
pg.py [rllib] Pull out experimental dsl into rllib.execution module, add initial unit tests (#7958) 2020-04-10 00:56:08 -07:00
pg_tf_policy.py [rllib] implemented compute_advantages without gae (#6941) 2020-01-31 22:25:45 -08:00
pg_torch_policy.py [RLlib] Exploration API: merge deterministic flag with exploration classes (SoftQ and StochasticSampling). (#7155) 2020-02-19 12:18:45 -08:00