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https://github.com/vale981/ray
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428516056a
* 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>
48 lines
1.7 KiB
YAML
48 lines
1.7 KiB
YAML
# Run e.g. on a g3.16xlarge (4 GPUs) with `num_gpus=1` (1 for each trial;
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# MsPacman torch + tf; Pong torch + tf).
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# Uses the hyperparameters published in [2] (see rllib/agents/sac/README.md).
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atari-sac-tf-and-torch:
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env:
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grid_search:
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- MsPacmanNoFrameskip-v4
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- PongNoFrameskip-v4
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run: SAC
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stop:
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timesteps_total: 20000000
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config:
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# Works for both torch and tf.
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use_pytorch:
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grid_search: [false, true]
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gamma: 0.99
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# state-preprocessor=Our default Atari Conv2D-net.
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use_state_preprocessor: true
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Q_model:
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hidden_activation: relu
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hidden_layer_sizes: [512]
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policy_model:
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hidden_activation: relu
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hidden_layer_sizes: [512]
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# Do hard syncs.
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# Soft-syncs seem to work less reliably for discrete action spaces.
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tau: 1.0
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target_network_update_freq: 8000
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# auto = 0.98 * -log(1/|A|)
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target_entropy: auto
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clip_rewards: 1.0
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no_done_at_end: False
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n_step: 1
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rollout_fragment_length: 1
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prioritized_replay: true
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train_batch_size: 64
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timesteps_per_iteration: 4
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# Paper uses 20k random timesteps, which is not exactly the same, but
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# seems to work nevertheless. We use 100k here for the longer Atari
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# runs (DQN style: filling up the buffer a bit before learning).
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learning_starts: 100000
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optimization:
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actor_learning_rate: 0.0003
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critic_learning_rate: 0.0003
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entropy_learning_rate: 0.0003
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num_workers: 0
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num_gpus: 1
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metrics_smoothing_episodes: 5
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