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>
136 lines
3.7 KiB
YAML
136 lines
3.7 KiB
YAML
apex-breakoutnoframeskip-v4:
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env: BreakoutNoFrameskip-v4
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run: APEX
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frameworks: [ "tf", "tf2", "torch" ]
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stop:
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time_total_s: 3600
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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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frameworks: [ "tf", "tf2", "torch" ]
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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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# Bring cql test back after we make sure it learns.
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#cql-halfcheetahbulletenv-v0:
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# env: HalfCheetahBulletEnv-v0
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# run: CQL
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# frameworks: [ "tf", "tf2", "torch" ]
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# stop:
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# time_total_s: 1800
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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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sac-halfcheetahbulletenv-v0:
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env: HalfCheetahBulletEnv-v0
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run: SAC
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frameworks: [ "tf", "tf2", "torch" ]
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stop:
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time_total_s: 3600
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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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