Initial commit
Browse files- .gitattributes +1 -0
- README.md +65 -0
- args.yml +75 -0
- config.yml +21 -0
- env_kwargs.yml +1 -0
- replay.mp4 +3 -0
- results.json +1 -0
- sac-seals-Swimmer-v0.zip +3 -0
- sac-seals-Swimmer-v0/_stable_baselines3_version +1 -0
- sac-seals-Swimmer-v0/actor.optimizer.pth +3 -0
- sac-seals-Swimmer-v0/critic.optimizer.pth +3 -0
- sac-seals-Swimmer-v0/data +120 -0
- sac-seals-Swimmer-v0/ent_coef_optimizer.pth +3 -0
- sac-seals-Swimmer-v0/policy.pth +3 -0
- sac-seals-Swimmer-v0/pytorch_variables.pth +3 -0
- sac-seals-Swimmer-v0/system_info.txt +7 -0
- train_eval_metrics.zip +3 -0
.gitattributes
CHANGED
@@ -29,3 +29,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.mp4 filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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library_name: stable-baselines3
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tags:
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- seals/Swimmer-v0
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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model-index:
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- name: SAC
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results:
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- metrics:
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- type: mean_reward
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value: 35.88 +/- 2.02
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name: mean_reward
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task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: seals/Swimmer-v0
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type: seals/Swimmer-v0
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---
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# **SAC** Agent playing **seals/Swimmer-v0**
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This is a trained model of a **SAC** agent playing **seals/Swimmer-v0**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
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The RL Zoo is a training framework for Stable Baselines3
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reinforcement learning agents,
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with hyperparameter optimization and pre-trained agents included.
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## Usage (with SB3 RL Zoo)
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RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
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SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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```
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# Download model and save it into the logs/ folder
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python -m utils.load_from_hub --algo sac --env seals/Swimmer-v0 -orga HumanCompatibleAI -f logs/
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python enjoy.py --algo sac --env seals/Swimmer-v0 -f logs/
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```
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## Training (with the RL Zoo)
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```
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python train.py --algo sac --env seals/Swimmer-v0 -f logs/
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# Upload the model and generate video (when possible)
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python -m utils.push_to_hub --algo sac --env seals/Swimmer-v0 -f logs/ -orga HumanCompatibleAI
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```
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## Hyperparameters
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```python
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OrderedDict([('batch_size', 128),
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('buffer_size', 100000),
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('gamma', 0.995),
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('learning_rate', 0.00039981805535514633),
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('learning_starts', 1000),
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('n_timesteps', 1000000.0),
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('policy', 'MlpPolicy'),
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('policy_kwargs',
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'dict(net_arch=[400, 300], log_std_init=-2.689958330139309)'),
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('tau', 0.01),
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('train_freq', 256),
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('normalize', False)])
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```
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args.yml
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!!python/object/apply:collections.OrderedDict
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- - - algo
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- sac
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- - device
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+
- cpu
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+
- - env
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+
- seals/Swimmer-v0
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- - env_kwargs
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+
- null
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+
- - eval_episodes
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+
- 5
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+
- - eval_freq
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- 25000
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- - gym_packages
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- - seals
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- - hyperparams
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- null
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- - log_folder
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- seals_experts_wandb_oldpickle/seed_5/
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- - log_interval
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+
- -1
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+
- - max_total_trials
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- null
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+
- - n_eval_envs
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- 1
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- - n_evaluations
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- null
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- - n_jobs
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- 1
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- - n_startup_trials
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+
- 10
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- - n_timesteps
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- -1
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- - n_trials
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- 500
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- - no_optim_plots
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- false
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- - num_threads
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- 1
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- - optimization_log_path
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- null
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- - optimize_hyperparameters
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- false
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+
- - pruner
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- median
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- - sampler
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- tpe
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- - save_freq
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- -1
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- - save_replay_buffer
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- false
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+
- - seed
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+
- 5
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+
- - storage
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- null
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- - study_name
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- null
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- - tensorboard_log
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- runs/seals/Swimmer-v0__sac__5__1658841516
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- - track
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- true
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- - trained_agent
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- ''
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- - truncate_last_trajectory
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+
- true
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+
- - uuid
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+
- false
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+
- - vec_env
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+
- dummy
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+
- - verbose
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- 1
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+
- - wandb_entity
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+
- null
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+
- - wandb_project_name
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+
- seals-experts-oldpickle
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config.yml
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+
!!python/object/apply:collections.OrderedDict
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- - - batch_size
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- 128
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+
- - buffer_size
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+
- 100000
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+
- - gamma
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+
- 0.995
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+
- - learning_rate
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+
- 0.00039981805535514633
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+
- - learning_starts
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+
- 1000
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+
- - n_timesteps
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+
- 1000000.0
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+
- - policy
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+
- MlpPolicy
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+
- - policy_kwargs
|
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+
- dict(net_arch=[400, 300], log_std_init=-2.689958330139309)
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+
- - tau
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+
- 0.01
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+
- - train_freq
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- 256
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env_kwargs.yml
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{}
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replay.mp4
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:2734d573e8abd0607cda70ce0e28a83ea8d3cd561747a1d58a55d791ee947f2f
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+
size 425474
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results.json
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{"mean_reward": 35.87843099999999, "std_reward": 2.0229172814859733, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-27T17:13:28.059429"}
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sac-seals-Swimmer-v0.zip
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:c273458046da9d86b10ebe901f57ccb4a12d31f64019cab1b177f94f08b4ab45
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size 5585012
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sac-seals-Swimmer-v0/_stable_baselines3_version
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1.6.0
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sac-seals-Swimmer-v0/actor.optimizer.pth
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version https://git-lfs.github.com/spec/v1
|
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+
oid sha256:3dc3ff9ce8d770eec19b754546ae239d876736d80282c894436a658ae0892643
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size 1013341
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sac-seals-Swimmer-v0/critic.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:30116f753680549c64eb86f0d60e497bc07d4562924acd054cb97fabae90a91b
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size 2021689
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sac-seals-Swimmer-v0/data
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{
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"policy_class": {
|
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+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMCVNBQ1BvbGljeZSTlC4=",
|
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+
"__module__": "stable_baselines3.sac.policies",
|
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+
"__doc__": "\n Policy class (with both actor and critic) for SAC.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ",
|
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+
"__init__": "<function SACPolicy.__init__ at 0x7f564e7b30d0>",
|
8 |
+
"_build": "<function SACPolicy._build at 0x7f564e7b3160>",
|
9 |
+
"_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f564e7b31f0>",
|
10 |
+
"reset_noise": "<function SACPolicy.reset_noise at 0x7f564e7b3280>",
|
11 |
+
"make_actor": "<function SACPolicy.make_actor at 0x7f564e7b3310>",
|
12 |
+
"make_critic": "<function SACPolicy.make_critic at 0x7f564e7b33a0>",
|
13 |
+
"forward": "<function SACPolicy.forward at 0x7f564e7b3430>",
|
14 |
+
"_predict": "<function SACPolicy._predict at 0x7f564e7b34c0>",
|
15 |
+
"set_training_mode": "<function SACPolicy.set_training_mode at 0x7f564e7b3550>",
|
16 |
+
"__abstractmethods__": "frozenset()",
|
17 |
+
"_abc_impl": "<_abc_data object at 0x7f564e7ac900>"
|
18 |
+
},
|
19 |
+
"verbose": 1,
|
20 |
+
"policy_kwargs": {
|
21 |
+
"net_arch": [
|
22 |
+
400,
|
23 |
+
300
|
24 |
+
],
|
25 |
+
"log_std_init": -2.689958330139309,
|
26 |
+
"use_sde": false
|
27 |
+
},
|
28 |
+
"observation_space": {
|
29 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
30 |
+
":serialized:": "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",
|
31 |
+
"dtype": "float64",
|
32 |
+
"_shape": [
|
33 |
+
10
|
34 |
+
],
|
35 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
|
36 |
+
"high": "[inf inf inf inf inf inf inf inf inf inf]",
|
37 |
+
"bounded_below": "[False False False False False False False False False False]",
|
38 |
+
"bounded_above": "[False False False False False False False False False False]",
|
39 |
+
"_np_random": null
|
40 |
+
},
|
41 |
+
"action_space": {
|
42 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
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sac-seals-Swimmer-v0/ent_coef_optimizer.pth
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OS: Linux-5.4.0-122-generic-x86_64-with-glibc2.29 #138-Ubuntu SMP Wed Jun 22 15:00:31 UTC 2022
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