Quentin Gallouédec
commited on
Commit
•
c8d0213
1
Parent(s):
3dd6f30
Initial commit
Browse files- .gitattributes +1 -0
- README.md +88 -0
- args.yml +83 -0
- config.yml +32 -0
- env_kwargs.yml +1 -0
- replay.mp4 +3 -0
- results.json +1 -0
- tqc-PandaReach-v1.zip +3 -0
- tqc-PandaReach-v1/_stable_baselines3_version +1 -0
- tqc-PandaReach-v1/actor.optimizer.pth +3 -0
- tqc-PandaReach-v1/critic.optimizer.pth +3 -0
- tqc-PandaReach-v1/data +129 -0
- tqc-PandaReach-v1/ent_coef_optimizer.pth +3 -0
- tqc-PandaReach-v1/policy.pth +3 -0
- tqc-PandaReach-v1/pytorch_variables.pth +3 -0
- tqc-PandaReach-v1/system_info.txt +7 -0
- train_eval_metrics.zip +3 -0
- vec_normalize.pkl +3 -0
.gitattributes
CHANGED
@@ -32,3 +32,4 @@ 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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*.zst 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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*.zst 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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- PandaReach-v1
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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: TQC
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results:
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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: PandaReach-v1
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type: PandaReach-v1
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metrics:
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- type: mean_reward
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value: -2.00 +/- 0.77
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name: mean_reward
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verified: false
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---
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# **TQC** Agent playing **PandaReach-v1**
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This is a trained model of a **TQC** agent playing **PandaReach-v1**
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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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Install the RL Zoo (with SB3 and SB3-Contrib):
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```bash
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pip install rl_zoo3
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```
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```
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# Download model and save it into the logs/ folder
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python -m rl_zoo3.load_from_hub --algo tqc --env PandaReach-v1 -orga qgallouedec -f logs/
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python -m rl_zoo3.enjoy --algo tqc --env PandaReach-v1 -f logs/
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```
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If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
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```
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python -m rl_zoo3.load_from_hub --algo tqc --env PandaReach-v1 -orga qgallouedec -f logs/
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python -m rl_zoo3.enjoy --algo tqc --env PandaReach-v1 -f logs/
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```
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## Training (with the RL Zoo)
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```
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python -m rl_zoo3.train --algo tqc --env PandaReach-v1 -f logs/
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# Upload the model and generate video (when possible)
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python -m rl_zoo3.push_to_hub --algo tqc --env PandaReach-v1 -f logs/ -orga qgallouedec
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```
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## Hyperparameters
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```python
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OrderedDict([('batch_size', 256),
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('buffer_size', 1000000),
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('ent_coef', 'auto'),
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('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
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('gamma', 0.95),
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('gradient_steps', -1),
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('learning_rate', 0.001),
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('learning_starts', 1000),
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('n_envs', 8),
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('n_timesteps', 20000.0),
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('normalize', True),
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('policy', 'MultiInputPolicy'),
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('policy_kwargs', 'dict(net_arch=[64, 64], n_critics=1)'),
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('replay_buffer_class', 'HerReplayBuffer'),
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('replay_buffer_kwargs',
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"dict( online_sampling=True, goal_selection_strategy='future', "
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'n_sampled_goal=4 )'),
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('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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```
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# Environment Arguments
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```python
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{'render': True}
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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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- tqc
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- - conf_file
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- null
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- - device
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- auto
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- - env
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- PandaReach-v1
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- - env_kwargs
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- null
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- - eval_episodes
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- 20
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- - eval_freq
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- 25000
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- - gym_packages
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- []
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- - hyperparams
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- n_envs: 8
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+
- - log_folder
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- logs
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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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- 5
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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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+
- - progress
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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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+
- 750406541
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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/PandaReach-v1__tqc__750406541__1676298577
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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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- qgallouedec
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+
- - wandb_project_name
|
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+
- vec-her-sb3
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- - wandb_tags
|
81 |
+
- []
|
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+
- - yaml_file
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+
- null
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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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3 |
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- 256
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4 |
+
- - buffer_size
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5 |
+
- 1000000
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6 |
+
- - ent_coef
|
7 |
+
- auto
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8 |
+
- - env_wrapper
|
9 |
+
- sb3_contrib.common.wrappers.TimeFeatureWrapper
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10 |
+
- - gamma
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11 |
+
- 0.95
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+
- - gradient_steps
|
13 |
+
- -1
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+
- - learning_rate
|
15 |
+
- 0.001
|
16 |
+
- - learning_starts
|
17 |
+
- 1000
|
18 |
+
- - n_envs
|
19 |
+
- 8
|
20 |
+
- - n_timesteps
|
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+
- 20000.0
|
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+
- - normalize
|
23 |
+
- true
|
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+
- - policy
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+
- MultiInputPolicy
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+
- - policy_kwargs
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+
- dict(net_arch=[64, 64], n_critics=1)
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+
- - replay_buffer_class
|
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+
- HerReplayBuffer
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+
- - replay_buffer_kwargs
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+
- dict( online_sampling=True, goal_selection_strategy='future', n_sampled_goal=4
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+
)
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env_kwargs.yml
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render: true
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replay.mp4
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:b85f8e2bddd6983d05428ac62403f5f786d989c52ad054e263e92c34b0f7b52b
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size 603661
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results.json
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{"mean_reward": -2.0, "std_reward": 0.7745966692414834, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-27T16:55:31.812586"}
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tqc-PandaReach-v1.zip
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:63b2dd3341a97bf2a91fb1f233a1ba8bd9a8f03971d9ed1ff79710b7bd04c436
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+
size 225612
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tqc-PandaReach-v1/_stable_baselines3_version
ADDED
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1 |
+
1.8.0a6
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tqc-PandaReach-v1/actor.optimizer.pth
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+
version https://git-lfs.github.com/spec/v1
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+
oid sha256:d5a59652ef64878e69a4b65b4f699a896cdea68d382998ac22e4914424b4af8b
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+
size 50077
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tqc-PandaReach-v1/critic.optimizer.pth
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+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6800b17440e2a6a641af6e258c5ee31b42dba601d658126a3c711bd40101ff7b
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3 |
+
size 59951
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tqc-PandaReach-v1/data
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+
{
|
2 |
+
"policy_class": {
|
3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gAWVMQAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu",
|
5 |
+
"__module__": "sb3_contrib.tqc.policies",
|
6 |
+
"__doc__": "\n Policy class (with both actor and critic) for TQC.\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 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 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_quantiles: Number of quantiles for the critic.\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 ",
|
7 |
+
"__init__": "<function MultiInputPolicy.__init__ at 0x7f20c3123a60>",
|
8 |
+
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f20c3124880>"
|
10 |
+
},
|
11 |
+
"verbose": 1,
|
12 |
+
"policy_kwargs": {
|
13 |
+
"net_arch": [
|
14 |
+
64,
|
15 |
+
64
|
16 |
+
],
|
17 |
+
"n_critics": 1,
|
18 |
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"use_sde": false
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},
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"_n_updates": 19000,
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"buffer_size": 1,
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"batch_size": 256,
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"learning_starts": 1000,
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"tau": 0.005,
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"gamma": 0.95,
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"gradient_steps": -1,
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"optimize_memory_usage": false,
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"replay_buffer_class": {
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":type:": "<class 'abc.ABCMeta'>",
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"__module__": "stable_baselines3.her.her_replay_buffer",
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"__doc__": "\n Hindsight Experience Replay (HER) buffer.\n Paper: https://arxiv.org/abs/1707.01495\n\n .. warning::\n\n For performance reasons, the maximum number of steps per episodes must be specified.\n In most cases, it will be inferred if you specify ``max_episode_steps`` when registering the environment\n or if you use a ``gym.wrappers.TimeLimit`` (and ``env.spec`` is not None).\n Otherwise, you can directly pass ``max_episode_length`` to the replay buffer constructor.\n\n\n Replay buffer for sampling HER (Hindsight Experience Replay) transitions.\n In the online sampling case, these new transitions will not be saved in the replay buffer\n and will only be created at sampling time.\n\n :param env: The training environment\n :param buffer_size: The size of the buffer measured in transitions.\n :param max_episode_length: The maximum length of an episode. If not specified,\n it will be automatically inferred if the environment uses a ``gym.wrappers.TimeLimit`` wrapper.\n :param goal_selection_strategy: Strategy for sampling goals for replay.\n One of ['episode', 'final', 'future']\n :param device: PyTorch device\n :param n_sampled_goal: Number of virtual transitions to create per real transition,\n by sampling new goals.\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ",
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"__init__": "<function HerReplayBuffer.__init__ at 0x7f20c35b5af0>",
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"__getstate__": "<function HerReplayBuffer.__getstate__ at 0x7f20c35b5b80>",
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"__setstate__": "<function HerReplayBuffer.__setstate__ at 0x7f20c35b5c10>",
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"set_env": "<function HerReplayBuffer.set_env at 0x7f20c35b5ca0>",
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"_get_samples": "<function HerReplayBuffer._get_samples at 0x7f20c35b5d30>",
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"sample": "<function HerReplayBuffer.sample at 0x7f20c35b5dc0>",
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"_sample_offline": "<function HerReplayBuffer._sample_offline at 0x7f20c35b5e50>",
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"sample_goals": "<function HerReplayBuffer.sample_goals at 0x7f20c35b5ee0>",
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"_sample_transitions": "<function HerReplayBuffer._sample_transitions at 0x7f20c35b5f70>",
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"add": "<function HerReplayBuffer.add at 0x7f20c3547040>",
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"store_episode": "<function HerReplayBuffer.store_episode at 0x7f20c35470d0>",
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"_sample_her_transitions": "<function HerReplayBuffer._sample_her_transitions at 0x7f20c3547160>",
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"n_episodes_stored": "<property object at 0x7f20c35452c0>",
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"size": "<function HerReplayBuffer.size at 0x7f20c3547280>",
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"reset": "<function HerReplayBuffer.reset at 0x7f20c3547310>",
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"truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x7f20c35473a0>",
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"__abstractmethods__": "frozenset()",
|
111 |
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"_abc_impl": "<_abc._abc_data object at 0x7f20c35467c0>"
|
112 |
+
},
|
113 |
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"replay_buffer_kwargs": {
|
114 |
+
"online_sampling": true,
|
115 |
+
"goal_selection_strategy": "future",
|
116 |
+
"n_sampled_goal": 4
|
117 |
+
},
|
118 |
+
"train_freq": {
|
119 |
+
":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
|
120 |
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":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"
|
121 |
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},
|
122 |
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"use_sde_at_warmup": false,
|
123 |
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"target_entropy": -3.0,
|
124 |
+
"ent_coef": "auto",
|
125 |
+
"target_update_interval": 1,
|
126 |
+
"top_quantiles_to_drop_per_net": 2,
|
127 |
+
"batch_norm_stats": [],
|
128 |
+
"batch_norm_stats_target": []
|
129 |
+
}
|
tqc-PandaReach-v1/ent_coef_optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:4ddd8cb557d3e203d0674c579e7eb87b2de139df4b5b019dd2e4a274d29024a6
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3 |
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size 1507
|
tqc-PandaReach-v1/policy.pth
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:2132b76bb1e30904bffad68a21d6ee732b065a348c9e90a54c1c58f9d2911cc9
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3 |
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size 83945
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tqc-PandaReach-v1/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:5306db0cf4e0e402436d8757b79adbca1eb207cc4872a2b908c2f33c8c882777
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size 747
|
tqc-PandaReach-v1/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Linux-5.19.0-32-generic-x86_64-with-glibc2.35 # 33~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon Jan 30 17:03:34 UTC 2
|
2 |
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- Python: 3.9.12
|
3 |
+
- Stable-Baselines3: 1.8.0a6
|
4 |
+
- PyTorch: 1.13.1+cu117
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.24.1
|
7 |
+
- Gym: 0.21.0
|
train_eval_metrics.zip
ADDED
@@ -0,0 +1,3 @@
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|
1 |
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version https://git-lfs.github.com/spec/v1
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size 10999
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vec_normalize.pkl
ADDED
@@ -0,0 +1,3 @@
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|
|
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|
|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:de03886ccdebb7bd644e3041ef5d7e588aefc1a1e9dd217f6f9e71af59ec07fd
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size 5469
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