Quentin Gallouédec
commited on
Commit
•
c117902
1
Parent(s):
513dfc4
Initial commit
Browse files- .gitattributes +1 -0
- README.md +79 -0
- args.yml +79 -0
- config.yml +27 -0
- env_kwargs.yml +1 -0
- replay.mp4 +3 -0
- results.json +1 -0
- tqc-MountainCarContinuous-v0.zip +3 -0
- tqc-MountainCarContinuous-v0/_stable_baselines3_version +1 -0
- tqc-MountainCarContinuous-v0/actor.optimizer.pth +3 -0
- tqc-MountainCarContinuous-v0/critic.optimizer.pth +3 -0
- tqc-MountainCarContinuous-v0/data +125 -0
- tqc-MountainCarContinuous-v0/policy.pth +3 -0
- tqc-MountainCarContinuous-v0/pytorch_variables.pth +3 -0
- tqc-MountainCarContinuous-v0/system_info.txt +7 -0
- train_eval_metrics.zip +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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- MountainCarContinuous-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: 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: MountainCarContinuous-v0
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type: MountainCarContinuous-v0
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metrics:
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- type: mean_reward
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value: 90.28 +/- 0.49
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name: mean_reward
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verified: false
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---
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# **TQC** Agent playing **MountainCarContinuous-v0**
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This is a trained model of a **TQC** agent playing **MountainCarContinuous-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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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 MountainCarContinuous-v0 -orga qgallouedec -f logs/
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python -m rl_zoo3.enjoy --algo tqc --env MountainCarContinuous-v0 -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 MountainCarContinuous-v0 -orga qgallouedec -f logs/
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python -m rl_zoo3.enjoy --algo tqc --env MountainCarContinuous-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 -m rl_zoo3.train --algo tqc --env MountainCarContinuous-v0 -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 MountainCarContinuous-v0 -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', 512),
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('buffer_size', 50000),
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('ent_coef', 0.1),
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('gamma', 0.9999),
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('gradient_steps', 32),
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('learning_rate', 0.0003),
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('learning_starts', 0),
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('n_timesteps', 50000.0),
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('policy', 'MlpPolicy'),
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('policy_kwargs', 'dict(log_std_init=-3.67, net_arch=[64, 64])'),
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('tau', 0.01),
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('train_freq', 32),
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('use_sde', True),
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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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- tqc
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- - device
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- auto
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- - env
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- MountainCarContinuous-v0
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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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- null
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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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- 2181305313
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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/MountainCarContinuous-v0__tqc__2181305313__1670944334
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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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+
- openrlbenchmark
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+
- - wandb_project_name
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+
- sb3
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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 |
+
- 512
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4 |
+
- - buffer_size
|
5 |
+
- 50000
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6 |
+
- - ent_coef
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7 |
+
- 0.1
|
8 |
+
- - gamma
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9 |
+
- 0.9999
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10 |
+
- - gradient_steps
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11 |
+
- 32
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12 |
+
- - learning_rate
|
13 |
+
- 0.0003
|
14 |
+
- - learning_starts
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+
- 0
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+
- - n_timesteps
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+
- 50000.0
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+
- - policy
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+
- MlpPolicy
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+
- - policy_kwargs
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+
- dict(log_std_init=-3.67, net_arch=[64, 64])
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+
- - tau
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+
- 0.01
|
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+
- - train_freq
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+
- 32
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+
- - use_sde
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+
- true
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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
|
2 |
+
oid sha256:240c7652a9ab9f5e9e3af96bf0b862856799adcd2f419f9b0439f87632d1f112
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+
size 264456
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results.json
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{"mean_reward": 90.28224399999999, "std_reward": 0.4911590969144292, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-27T16:39:07.970835"}
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tqc-MountainCarContinuous-v0.zip
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version https://git-lfs.github.com/spec/v1
|
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+
oid sha256:1bc60f984e92ff41af92317056e801bd6989e12890393f8a801cc981945277b3
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+
size 292940
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tqc-MountainCarContinuous-v0/_stable_baselines3_version
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+
1.8.0a6
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tqc-MountainCarContinuous-v0/actor.optimizer.pth
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+
version https://git-lfs.github.com/spec/v1
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+
oid sha256:11c13371a2462186d0199028d1d29823ffd55581f7c4674a9fe23a41870b7554
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+
size 41702
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tqc-MountainCarContinuous-v0/critic.optimizer.pth
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1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
+
oid sha256:d4954f266fdc604c5a4f349072c23d6a45579d811b02d19ea83c18cd60952eed
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3 |
+
size 106169
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tqc-MountainCarContinuous-v0/data
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{
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2 |
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"policy_class": {
|
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+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gAWVKgAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMCVRRQ1BvbGljeZSTlC4=",
|
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 features_extractor_kwargs: Keyword arguments\n to pass to the feature 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_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 TQCPolicy.__init__ at 0x7f9caad234c0>",
|
8 |
+
"_build": "<function TQCPolicy._build at 0x7f9caad23550>",
|
9 |
+
"_get_constructor_parameters": "<function TQCPolicy._get_constructor_parameters at 0x7f9caad235e0>",
|
10 |
+
"reset_noise": "<function TQCPolicy.reset_noise at 0x7f9caad23670>",
|
11 |
+
"make_actor": "<function TQCPolicy.make_actor at 0x7f9caad23700>",
|
12 |
+
"make_critic": "<function TQCPolicy.make_critic at 0x7f9caad23790>",
|
13 |
+
"forward": "<function TQCPolicy.forward at 0x7f9caad23820>",
|
14 |
+
"_predict": "<function TQCPolicy._predict at 0x7f9caad238b0>",
|
15 |
+
"set_training_mode": "<function TQCPolicy.set_training_mode at 0x7f9caad23940>",
|
16 |
+
"__abstractmethods__": "frozenset()",
|
17 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f9caad24100>"
|
18 |
+
},
|
19 |
+
"verbose": 1,
|
20 |
+
"policy_kwargs": {
|
21 |
+
"log_std_init": -3.67,
|
22 |
+
"net_arch": [
|
23 |
+
64,
|
24 |
+
64
|
25 |
+
],
|
26 |
+
"use_sde": true
|
27 |
+
},
|
28 |
+
"observation_space": {
|
29 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
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"__doc__": "\n Replay buffer used in off-policy algorithms like SAC/TD3.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n of the replay buffer which reduces by almost a factor two the memory used,\n at a cost of more complexity.\n See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n Cannot be used in combination with handle_timeout_termination.\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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|
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|
tqc-MountainCarContinuous-v0/policy.pth
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 125000
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tqc-MountainCarContinuous-v0/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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tqc-MountainCarContinuous-v0/system_info.txt
ADDED
@@ -0,0 +1,7 @@
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- 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
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- Python: 3.9.12
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- Stable-Baselines3: 1.8.0a6
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- PyTorch: 1.13.1+cu117
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- GPU Enabled: True
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- Numpy: 1.24.1
|
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- Gym: 0.21.0
|
train_eval_metrics.zip
ADDED
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