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ray/rllib/tuned_examples/regression_tests/cartpole-sac-torch.yaml

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[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
cartpole-sac-torch:
[RLlib] SAC add discrete action support. (#7320) * Exploration API (+EpsilonGreedy sub-class). * Exploration API (+EpsilonGreedy sub-class). * Cleanup/LINT. * Add `deterministic` to generic Trainer config (NOTE: this is still ignored by most Agents). * Add `error` option to deprecation_warning(). * WIP. * Bug fix: Get exploration-info for tf framework. Bug fix: Properly deprecate some DQN config keys. * WIP. * LINT. * WIP. * Split PerWorkerEpsilonGreedy out of EpsilonGreedy. Docstrings. * Fix bug in sampler.py in case Policy has self.exploration = None * Update rllib/agents/dqn/dqn.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * WIP. * Update rllib/agents/trainer.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * WIP. * Change requests. * LINT * In tune/utils/util.py::deep_update() Only keep deep_updat'ing if both original and value are dicts. If value is not a dict, set * Completely obsolete syn_replay_optimizer.py's parameters schedule_max_timesteps AND beta_annealing_fraction (replaced with prioritized_replay_beta_annealing_timesteps). * Update rllib/evaluation/worker_set.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * Review fixes. * Fix default value for DQN's exploration spec. * LINT * Fix recursion bug (wrong parent c'tor). * Do not pass timestep to get_exploration_info. * Update tf_policy.py * Fix some remaining issues with test cases and remove more deprecated DQN/APEX exploration configs. * Bug fix tf-action-dist * DDPG incompatibility bug fix with new DQN exploration handling (which is imported by DDPG). * Switch off exploration when getting action probs from off-policy-estimator's policy. * LINT * Fix test_checkpoint_restore.py. * Deprecate all SAC exploration (unused) configs. * Properly use `model.last_output()` everywhere. Instead of `model._last_output`. * WIP. * Take out set_epsilon from multi-agent-env test (not needed, decays anyway). * WIP. * Trigger re-test (flaky checkpoint-restore test). * WIP. * WIP. * Add test case for deterministic action sampling in PPO. * bug fix. * Added deterministic test cases for different Agents. * Fix problem with TupleActions in dynamic-tf-policy. * Separate supported_spaces tests so they can be run separately for easier debugging. * LINT. * Fix autoregressive_action_dist.py test case. * Re-test. * Fix. * Remove duplicate py_test rule from bazel. * LINT. * WIP. * WIP. * SAC fix. * SAC fix. * WIP. * WIP. * WIP. * FIX 2 examples tests. * WIP. * WIP. * WIP. * WIP. * WIP. * Fix. * LINT. * Renamed test file. * WIP. * Add unittest.main. * Make action_dist_class mandatory. * fix * FIX. * WIP. * WIP. * Fix. * Fix. * Fix explorations test case (contextlib cannot find its own nullcontext??). * Force torch to be installed for QMIX. * LINT. * Fix determine_tests_to_run.py. * Fix determine_tests_to_run.py. * WIP * Add Random exploration component to tests (fixed issue with "static-graph randomness" via py_function). * Add Random exploration component to tests (fixed issue with "static-graph randomness" via py_function). * Rename some stuff. * Rename some stuff. * WIP. * update. * WIP. * Gumbel Softmax Dist. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP * WIP. * WIP. * Hypertune. * Hypertune. * Hypertune. * Lock-in. * Cleanup. * LINT. * Fix. * Update rllib/policy/eager_tf_policy.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/agents/sac/sac_policy.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/agents/sac/sac_policy.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/models/tf/tf_action_dist.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/models/tf/tf_action_dist.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Fix items from review comments. * Add dm_tree to RLlib dependencies. * Add dm_tree to RLlib dependencies. * Fix DQN test cases ((Torch)Categorical). * Fix wrong pip install. Co-authored-by: Eric Liang <ekhliang@gmail.com> Co-authored-by: Kristian Hartikainen <kristian.hartikainen@gmail.com>
2020-03-06 19:37:12 +01:00
env: CartPole-v0
run: SAC
stop:
episode_reward_mean: 150
[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
timesteps_total: 50000
[RLlib] SAC add discrete action support. (#7320) * Exploration API (+EpsilonGreedy sub-class). * Exploration API (+EpsilonGreedy sub-class). * Cleanup/LINT. * Add `deterministic` to generic Trainer config (NOTE: this is still ignored by most Agents). * Add `error` option to deprecation_warning(). * WIP. * Bug fix: Get exploration-info for tf framework. Bug fix: Properly deprecate some DQN config keys. * WIP. * LINT. * WIP. * Split PerWorkerEpsilonGreedy out of EpsilonGreedy. Docstrings. * Fix bug in sampler.py in case Policy has self.exploration = None * Update rllib/agents/dqn/dqn.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * WIP. * Update rllib/agents/trainer.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * WIP. * Change requests. * LINT * In tune/utils/util.py::deep_update() Only keep deep_updat'ing if both original and value are dicts. If value is not a dict, set * Completely obsolete syn_replay_optimizer.py's parameters schedule_max_timesteps AND beta_annealing_fraction (replaced with prioritized_replay_beta_annealing_timesteps). * Update rllib/evaluation/worker_set.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * Review fixes. * Fix default value for DQN's exploration spec. * LINT * Fix recursion bug (wrong parent c'tor). * Do not pass timestep to get_exploration_info. * Update tf_policy.py * Fix some remaining issues with test cases and remove more deprecated DQN/APEX exploration configs. * Bug fix tf-action-dist * DDPG incompatibility bug fix with new DQN exploration handling (which is imported by DDPG). * Switch off exploration when getting action probs from off-policy-estimator's policy. * LINT * Fix test_checkpoint_restore.py. * Deprecate all SAC exploration (unused) configs. * Properly use `model.last_output()` everywhere. Instead of `model._last_output`. * WIP. * Take out set_epsilon from multi-agent-env test (not needed, decays anyway). * WIP. * Trigger re-test (flaky checkpoint-restore test). * WIP. * WIP. * Add test case for deterministic action sampling in PPO. * bug fix. * Added deterministic test cases for different Agents. * Fix problem with TupleActions in dynamic-tf-policy. * Separate supported_spaces tests so they can be run separately for easier debugging. * LINT. * Fix autoregressive_action_dist.py test case. * Re-test. * Fix. * Remove duplicate py_test rule from bazel. * LINT. * WIP. * WIP. * SAC fix. * SAC fix. * WIP. * WIP. * WIP. * FIX 2 examples tests. * WIP. * WIP. * WIP. * WIP. * WIP. * Fix. * LINT. * Renamed test file. * WIP. * Add unittest.main. * Make action_dist_class mandatory. * fix * FIX. * WIP. * WIP. * Fix. * Fix. * Fix explorations test case (contextlib cannot find its own nullcontext??). * Force torch to be installed for QMIX. * LINT. * Fix determine_tests_to_run.py. * Fix determine_tests_to_run.py. * WIP * Add Random exploration component to tests (fixed issue with "static-graph randomness" via py_function). * Add Random exploration component to tests (fixed issue with "static-graph randomness" via py_function). * Rename some stuff. * Rename some stuff. * WIP. * update. * WIP. * Gumbel Softmax Dist. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP * WIP. * WIP. * Hypertune. * Hypertune. * Hypertune. * Lock-in. * Cleanup. * LINT. * Fix. * Update rllib/policy/eager_tf_policy.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/agents/sac/sac_policy.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/agents/sac/sac_policy.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/models/tf/tf_action_dist.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/models/tf/tf_action_dist.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Fix items from review comments. * Add dm_tree to RLlib dependencies. * Add dm_tree to RLlib dependencies. * Fix DQN test cases ((Torch)Categorical). * Fix wrong pip install. Co-authored-by: Eric Liang <ekhliang@gmail.com> Co-authored-by: Kristian Hartikainen <kristian.hartikainen@gmail.com>
2020-03-06 19:37:12 +01:00
config:
[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
use_pytorch: true
[RLlib] SAC add discrete action support. (#7320) * Exploration API (+EpsilonGreedy sub-class). * Exploration API (+EpsilonGreedy sub-class). * Cleanup/LINT. * Add `deterministic` to generic Trainer config (NOTE: this is still ignored by most Agents). * Add `error` option to deprecation_warning(). * WIP. * Bug fix: Get exploration-info for tf framework. Bug fix: Properly deprecate some DQN config keys. * WIP. * LINT. * WIP. * Split PerWorkerEpsilonGreedy out of EpsilonGreedy. Docstrings. * Fix bug in sampler.py in case Policy has self.exploration = None * Update rllib/agents/dqn/dqn.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * WIP. * Update rllib/agents/trainer.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * WIP. * Change requests. * LINT * In tune/utils/util.py::deep_update() Only keep deep_updat'ing if both original and value are dicts. If value is not a dict, set * Completely obsolete syn_replay_optimizer.py's parameters schedule_max_timesteps AND beta_annealing_fraction (replaced with prioritized_replay_beta_annealing_timesteps). * Update rllib/evaluation/worker_set.py Co-Authored-By: Eric Liang <ekhliang@gmail.com> * Review fixes. * Fix default value for DQN's exploration spec. * LINT * Fix recursion bug (wrong parent c'tor). * Do not pass timestep to get_exploration_info. * Update tf_policy.py * Fix some remaining issues with test cases and remove more deprecated DQN/APEX exploration configs. * Bug fix tf-action-dist * DDPG incompatibility bug fix with new DQN exploration handling (which is imported by DDPG). * Switch off exploration when getting action probs from off-policy-estimator's policy. * LINT * Fix test_checkpoint_restore.py. * Deprecate all SAC exploration (unused) configs. * Properly use `model.last_output()` everywhere. Instead of `model._last_output`. * WIP. * Take out set_epsilon from multi-agent-env test (not needed, decays anyway). * WIP. * Trigger re-test (flaky checkpoint-restore test). * WIP. * WIP. * Add test case for deterministic action sampling in PPO. * bug fix. * Added deterministic test cases for different Agents. * Fix problem with TupleActions in dynamic-tf-policy. * Separate supported_spaces tests so they can be run separately for easier debugging. * LINT. * Fix autoregressive_action_dist.py test case. * Re-test. * Fix. * Remove duplicate py_test rule from bazel. * LINT. * WIP. * WIP. * SAC fix. * SAC fix. * WIP. * WIP. * WIP. * FIX 2 examples tests. * WIP. * WIP. * WIP. * WIP. * WIP. * Fix. * LINT. * Renamed test file. * WIP. * Add unittest.main. * Make action_dist_class mandatory. * fix * FIX. * WIP. * WIP. * Fix. * Fix. * Fix explorations test case (contextlib cannot find its own nullcontext??). * Force torch to be installed for QMIX. * LINT. * Fix determine_tests_to_run.py. * Fix determine_tests_to_run.py. * WIP * Add Random exploration component to tests (fixed issue with "static-graph randomness" via py_function). * Add Random exploration component to tests (fixed issue with "static-graph randomness" via py_function). * Rename some stuff. * Rename some stuff. * WIP. * update. * WIP. * Gumbel Softmax Dist. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP * WIP. * WIP. * Hypertune. * Hypertune. * Hypertune. * Lock-in. * Cleanup. * LINT. * Fix. * Update rllib/policy/eager_tf_policy.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/agents/sac/sac_policy.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/agents/sac/sac_policy.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/models/tf/tf_action_dist.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Update rllib/models/tf/tf_action_dist.py Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com> * Fix items from review comments. * Add dm_tree to RLlib dependencies. * Add dm_tree to RLlib dependencies. * Fix DQN test cases ((Torch)Categorical). * Fix wrong pip install. Co-authored-by: Eric Liang <ekhliang@gmail.com> Co-authored-by: Kristian Hartikainen <kristian.hartikainen@gmail.com>
2020-03-06 19:37:12 +01:00
gamma: 0.95
no_done_at_end: false
target_network_update_freq: 32
tau: 1.0
train_batch_size: 32
optimization:
actor_learning_rate: 0.005
critic_learning_rate: 0.005
entropy_learning_rate: 0.0001
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