mirror of
https://github.com/vale981/ray
synced 2025-03-06 18:41:40 -05:00
d8a61f801f
* Create a core set of algorithms tests to run nightly. * Run release tests under tf, tf2, and torch frameworks. * Fix * Add eager_tracing option for tf2 framework. * make sure core tests can run in parallel. * cql * Report progress while running nightly/weekly tests. * Innclude SAC in nightly lineup. * Revert changes to learning_tests * rebrand to performance test. * update build_pipeline.py with new performance_tests name. * Record stats. * bug fix, need to populate experiments dict. * Alphabetize yaml files. * Allow specifying frameworks. And do not run tf2 by default. * remove some debugging code. * fix * Undo testing changes. * Do not run CQL regression for now. * LINT. Co-authored-by: sven1977 <svenmika1977@gmail.com>
470 lines
13 KiB
YAML
470 lines
13 KiB
YAML
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a2c-breakoutnoframeskip-v4:
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env: BreakoutNoFrameskip-v4
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run: A2C
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# Minimum reward and total ts (in given time_total_s) to pass this test.
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pass_criteria:
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episode_reward_mean: 50.0
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timesteps_total: 5000000
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stop:
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time_total_s: 7200
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config:
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rollout_fragment_length: 20
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clip_rewards: True
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num_workers: 5
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num_envs_per_worker: 5
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num_gpus: 1
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lr_schedule: [
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[0, 0.0007],
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[20000000, 0.000000000001],
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]
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# a3c-pongdeterministic-v4:
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# env: PongDeterministic-v4
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# run: A3C
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# # Minimum reward and total ts (in given time_total_s) to pass this test.
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# pass_criteria:
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# episode_reward_mean: 18.0
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# timesteps_total: 5000000
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# stop:
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# time_total_s: 3600
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# config:
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# ignore_worker_failures: true
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# num_gpus: 0
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# num_workers: 16
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# rollout_fragment_length: 20
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# vf_loss_coeff: 0.5
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# entropy_coeff: 0.01
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# gamma: 0.99
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# grad_clip: 40.0
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# lambda: 1.0
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# lr: 0.0001
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# observation_filter: NoFilter
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# preprocessor_pref: rllib
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# model:
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# use_lstm: true
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# conv_activation: elu
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# dim: 42
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# grayscale: true
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# zero_mean: false
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# # Reduced channel depth and kernel size from default.
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# conv_filters: [
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# [32, [3, 3], 2],
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# [32, [3, 3], 2],
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# [32, [3, 3], 2],
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# [32, [3, 3], 2],
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# ]
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apex-breakoutnoframeskip-v4:
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env: BreakoutNoFrameskip-v4
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run: APEX
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# Minimum reward and total ts (in given time_total_s) to pass this test.
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pass_criteria:
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episode_reward_mean: 20.0
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timesteps_total: 7000000
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stop:
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time_total_s: 7200
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config:
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double_q: false
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dueling: false
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num_atoms: 1
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noisy: false
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n_step: 3
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lr: .0001
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adam_epsilon: .00015
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hiddens: [512]
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buffer_size: 1000000
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exploration_config:
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epsilon_timesteps: 200000
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final_epsilon: 0.01
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prioritized_replay_alpha: 0.5
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final_prioritized_replay_beta: 1.0
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prioritized_replay_beta_annealing_timesteps: 2000000
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num_gpus: 1
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num_workers: 8
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num_envs_per_worker: 8
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rollout_fragment_length: 20
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train_batch_size: 512
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target_network_update_freq: 50000
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timesteps_per_iteration: 25000
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appo-pongnoframeskip-v4:
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env: PongNoFrameskip-v4
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run: APPO
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# Minimum reward and total ts (in given time_total_s) to pass this test.
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pass_criteria:
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episode_reward_mean: 18.0
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timesteps_total: 5000000
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stop:
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time_total_s: 2000
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config:
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vtrace: True
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use_kl_loss: False
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rollout_fragment_length: 50
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train_batch_size: 750
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num_workers: 31
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broadcast_interval: 1
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max_sample_requests_in_flight_per_worker: 1
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num_multi_gpu_tower_stacks: 1
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num_envs_per_worker: 8
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num_sgd_iter: 2
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vf_loss_coeff: 1.0
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clip_param: 0.3
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num_gpus: 1
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grad_clip: 10
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model:
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dim: 42
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# ARS was never tested/tuned on Hopper. Maybe change to ReacherBulletEnv-v0?
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# ars-hopperbulletenv-v0:
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# env: HopperBulletEnv-v0
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# run: ARS
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# # Minimum reward and total ts (in given time_total_s) to pass this test.
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# pass_criteria:
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# episode_reward_mean: 100.0
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# timesteps_total: 2000000
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# stop:
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# time_total_s: 2000
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# config:
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# noise_stdev: 0.01
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# num_rollouts: 1
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# rollouts_used: 1
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# num_workers: 1
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# sgd_stepsize: 0.02
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# noise_size: 250000000
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# eval_prob: 0.2
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# offset: 0
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# observation_filter: NoFilter
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# report_length: 3
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# TODO: (sven) Fix all BC-dependent learning tests for cont. actions.
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# These seem quite hard to learn from the SAC-recorded HalfCheetahBulletEnv.
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# bc-halfcheetahbulletenv-v0:
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# env: HalfCheetahBulletEnv-v0
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# run: BC
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# pass_criteria:
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# episode_reward_mean: 400.0
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# timesteps_total: 10000000
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# stop:
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# time_total_s: 3600
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# config:
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# # Use input produced by expert SAC algo.
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# input: ["~/halfcheetah_expert_sac.zip"]
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# actions_in_input_normalized: true
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# num_gpus: 1
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# model:
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# fcnet_activation: relu
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# fcnet_hiddens: [256, 256, 256]
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# evaluation_num_workers: 1
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# evaluation_interval: 3
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# evaluation_config:
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# input: sampler
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# cql-halfcheetahbulletenv-v0:
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# env: HalfCheetahBulletEnv-v0
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# run: CQL
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# pass_criteria:
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# episode_reward_mean: 400.0
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# timesteps_total: 10000000
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# stop:
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# time_total_s: 3600
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# config:
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# # Use input produced by expert SAC algo.
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# input: ["~/halfcheetah_expert_sac.zip"]
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# actions_in_input_normalized: true
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#
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# soft_horizon: False
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# horizon: 1000
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# Q_model:
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# fcnet_activation: relu
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# fcnet_hiddens: [256, 256, 256]
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# policy_model:
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# fcnet_activation: relu
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# fcnet_hiddens: [256, 256, 256]
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# tau: 0.005
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# target_entropy: auto
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# no_done_at_end: false
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# n_step: 3
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# rollout_fragment_length: 1
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# prioritized_replay: false
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# train_batch_size: 256
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# target_network_update_freq: 0
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# timesteps_per_iteration: 1000
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# learning_starts: 256
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# optimization:
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# actor_learning_rate: 0.0001
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# critic_learning_rate: 0.0003
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# entropy_learning_rate: 0.0001
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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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#
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# # CQL Configs
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# min_q_weight: 5.0
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# bc_iters: 20000
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# temperature: 1.0
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# num_actions: 10
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# lagrangian: False
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#
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# # Switch on online evaluation.
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# evaluation_interval: 3
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# evaluation_config:
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# input: sampler
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ddpg-hopperbulletenv-v0:
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env: HopperBulletEnv-v0
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run: DDPG
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# Minimum reward and total ts (in given time_total_s) to pass this test.
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pass_criteria:
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episode_reward_mean: 110.0
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timesteps_total: 50000
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stop:
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time_total_s: 3600
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config:
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actor_hiddens: [256, 256]
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critic_hiddens: [256, 256]
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n_step: 3
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model: {}
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gamma: 0.99
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env_config: {}
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exploration_config:
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initial_scale: 1.0
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final_scale: 0.02
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scale_timesteps: 10000
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ou_base_scale: 0.1
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ou_theta: 0.15
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ou_sigma: 0.2
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timesteps_per_iteration: 1000
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target_network_update_freq: 0
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tau: 0.001
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buffer_size: 10000
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prioritized_replay: True
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prioritized_replay_alpha: 0.6
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prioritized_replay_beta: 0.4
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prioritized_replay_eps: 0.000001
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clip_rewards: false
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actor_lr: 0.001
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critic_lr: 0.001
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use_huber: true
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huber_threshold: 1.0
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l2_reg: 0.000001
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learning_starts: 500
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rollout_fragment_length: 1
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train_batch_size: 48
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num_gpus: 1
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num_workers: 0
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num_gpus_per_worker: 0
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worker_side_prioritization: false
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# Basically the same as atari-ppo, but adapted for DDPPO. Note that DDPPO
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# isn't actually any more efficient on Atari, since the network size is
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# relatively small and the env doesn't require a GPU.
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# ddppo-breakoutnoframeskip-v4:
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# env: BreakoutNoFrameskip-v4
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# run: DDPPO
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# # Minimum reward and total ts (in given time_total_s) to pass this test.
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# pass_criteria:
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# episode_reward_mean: 50.0
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# timesteps_total: 10000000
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# stop:
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# time_total_s: 3600
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# config:
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# # DDPPO only supports PyTorch so far.
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# frameworks: [ "torch" ]
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# # Worker config: 10 workers, each of which requires a GPU.
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# num_workers: 16
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# # Workers require GPUs, but share 1 GPU amongst 2 workers.
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# num_gpus_per_worker: 0.25
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# # Each worker will sample 100 * 5 envs per worker steps = 500 steps
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# # per optimization round. This is 5000 steps summed across workers.
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# rollout_fragment_length: 100
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# num_envs_per_worker: 5
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# # Each worker will take a minibatch of 50. There are 10 workers total,
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# # so the effective minibatch size will be 500.
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# sgd_minibatch_size: 50
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# num_sgd_iter: 30
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# # Params from standard PPO Atari config:
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# lambda: 0.95
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# kl_coeff: 0.5
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# clip_rewards: true
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# clip_param: 0.1
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# vf_loss_coeff: 0.1
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# vf_clip_param: 10.0
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# entropy_coeff: 0.01
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# batch_mode: truncate_episodes
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# observation_filter: NoFilter
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dqn-breakoutnoframeskip-v4:
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env: BreakoutNoFrameskip-v4
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run: DQN
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# Minimum reward and total ts (in given time_total_s) to pass this test.
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pass_criteria:
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episode_reward_mean: 20.0
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timesteps_total: 400000
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stop:
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time_total_s: 7200
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config:
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double_q: false
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dueling: false
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num_atoms: 1
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noisy: false
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prioritized_replay: false
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n_step: 1
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target_network_update_freq: 8000
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lr: .0000625
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adam_epsilon: .00015
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hiddens: [512]
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learning_starts: 20000
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buffer_size: 1000000
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rollout_fragment_length: 4
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train_batch_size: 32
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exploration_config:
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epsilon_timesteps: 200000
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final_epsilon: 0.01
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prioritized_replay_alpha: 0.5
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final_prioritized_replay_beta: 1.0
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prioritized_replay_beta_annealing_timesteps: 2000000
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num_gpus: 0.5
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timesteps_per_iteration: 10000
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es-humanoidbulletenv-v0:
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env: HumanoidBulletEnv-v0
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run: ES
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# Minimum reward and total ts (in given time_total_s) to pass this test.
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pass_criteria:
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episode_reward_mean: 100.0
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timesteps_total: 5000000
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stop:
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time_total_s: 3600
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config:
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num_workers: 50
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impala-breakoutnoframeskip-v4:
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env: BreakoutNoFrameskip-v4
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run: IMPALA
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# Minimum reward and total ts (in given time_total_s) to pass this test.
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pass_criteria:
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episode_reward_mean: 200.0
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timesteps_total: 6000000
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stop:
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time_total_s: 3600
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config:
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rollout_fragment_length: 50
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train_batch_size: 500
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num_workers: 10
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num_envs_per_worker: 5
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clip_rewards: True
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lr_schedule: [
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[0, 0.0005],
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[20000000, 0.000000000001],
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]
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num_gpus: 1
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# marwil-halfcheetahbulletenv-v0:
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# env: HalfCheetahBulletEnv-v0
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# run: MARWIL
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# pass_criteria:
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# episode_reward_mean: 400.0
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# timesteps_total: 10000000
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# stop:
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# time_total_s: 3600
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# config:
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# # Use input produced by expert SAC algo.
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# input: ["~/halfcheetah_expert_sac.zip"]
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# actions_in_input_normalized: true
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# # Switch off input evaluation (data does not contain action probs).
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# input_evaluation: []
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# num_gpus: 1
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# model:
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# fcnet_activation: relu
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# fcnet_hiddens: [256, 256, 256]
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# evaluation_num_workers: 1
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# evaluation_interval: 1
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# evaluation_config:
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# input: sampler
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ppo-breakoutnoframeskip-v4:
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env: BreakoutNoFrameskip-v4
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run: PPO
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# Minimum reward and total ts (in given time_total_s) to pass this test.
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pass_criteria:
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episode_reward_mean: 50.0
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timesteps_total: 7000000
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stop:
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time_total_s: 7200
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config:
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lambda: 0.95
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kl_coeff: 0.5
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clip_rewards: True
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clip_param: 0.1
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vf_clip_param: 10.0
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entropy_coeff: 0.01
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train_batch_size: 5000
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rollout_fragment_length: 100
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sgd_minibatch_size: 500
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num_sgd_iter: 10
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num_workers: 10
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num_envs_per_worker: 5
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batch_mode: truncate_episodes
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observation_filter: NoFilter
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model:
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vf_share_layers: true
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num_gpus: 1
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# Expect roughly 1000 reward after 1h on 1GPU
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sac-halfcheetahbulletenv-v0:
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env: HalfCheetahBulletEnv-v0
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run: SAC
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# Minimum reward and total ts (in given time_total_s) to pass this test.
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pass_criteria:
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episode_reward_mean: 500.0
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timesteps_total: 100000
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stop:
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time_total_s: 7200
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config:
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horizon: 1000
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soft_horizon: false
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Q_model:
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fcnet_activation: relu
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fcnet_hiddens: [256, 256]
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policy_model:
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fcnet_activation: relu
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fcnet_hiddens: [256, 256]
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tau: 0.005
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target_entropy: auto
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no_done_at_end: false
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n_step: 3
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rollout_fragment_length: 1
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prioritized_replay: true
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train_batch_size: 256
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target_network_update_freq: 1
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timesteps_per_iteration: 1000
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learning_starts: 10000
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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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td3-halfcheetahbulletenv-v0:
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env: HalfCheetahBulletEnv-v0
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run: TD3
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# Minimum reward and total ts (in given time_total_s) to pass this test.
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pass_criteria:
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episode_reward_mean: 400.0
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timesteps_total: 1000000
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stop:
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time_total_s: 7200
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config:
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num_gpus: 1
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learning_starts: 10000
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exploration_config:
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random_timesteps: 10000
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