Quentin Gallouédec
commited on
Commit
•
7d2d835
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Parent(s):
69494b2
Initial commit
Browse files- .gitattributes +1 -0
- README.md +71 -0
- args.yml +83 -0
- config.yml +11 -0
- ddpg-Ant-v3.zip +3 -0
- ddpg-Ant-v3/_stable_baselines3_version +1 -0
- ddpg-Ant-v3/actor.optimizer.pth +3 -0
- ddpg-Ant-v3/critic.optimizer.pth +3 -0
- ddpg-Ant-v3/data +120 -0
- ddpg-Ant-v3/policy.pth +3 -0
- ddpg-Ant-v3/pytorch_variables.pth +3 -0
- ddpg-Ant-v3/system_info.txt +7 -0
- env_kwargs.yml +1 -0
- replay.mp4 +3 -0
- results.json +1 -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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- Ant-v3
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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: DDPG
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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: Ant-v3
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type: Ant-v3
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metrics:
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- type: mean_reward
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value: 248.80 +/- 287.01
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name: mean_reward
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verified: false
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---
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# **DDPG** Agent playing **Ant-v3**
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This is a trained model of a **DDPG** agent playing **Ant-v3**
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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 ddpg --env Ant-v3 -orga qgallouedec -f logs/
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python -m rl_zoo3.enjoy --algo ddpg --env Ant-v3 -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 ddpg --env Ant-v3 -orga qgallouedec -f logs/
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python -m rl_zoo3.enjoy --algo ddpg --env Ant-v3 -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 ddpg --env Ant-v3 -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 ddpg --env Ant-v3 -f logs/ -orga qgallouedec
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```
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## Hyperparameters
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```python
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OrderedDict([('learning_starts', 10000),
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('n_timesteps', 1000000.0),
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('noise_std', 0.1),
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('noise_type', 'normal'),
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('policy', 'MlpPolicy'),
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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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- ddpg
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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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- Ant-v3
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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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- 1157720158
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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/Ant-v3__ddpg__1157720158__1676824465
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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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- - wandb_tags
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- []
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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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- - - learning_starts
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- 10000
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- - n_timesteps
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- 1000000.0
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+
- - noise_std
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- 0.1
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- - noise_type
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- normal
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- - policy
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- MlpPolicy
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ddpg-Ant-v3.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:b00b23a80193949776e1229a31a3c173fe1c97d8b49e195bf3c000b21ca3b4b2
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size 5421822
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ddpg-Ant-v3/_stable_baselines3_version
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1.8.0a6
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ddpg-Ant-v3/actor.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:ee25dcfb22cbe31f1bb8a47ddb2719c5122de44240ce6e418cdea31603d7ab26
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size 1344943
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ddpg-Ant-v3/critic.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:5155b685eb839fd408086425328613ddfd11404b995de98dcbee1eb9b2b75abc
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size 1353647
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ddpg-Ant-v3/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnRkMy5wb2xpY2llc5SMCVREM1BvbGljeZSTlC4=",
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+
"__module__": "stable_baselines3.td3.policies",
|
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"__doc__": "\n Policy class (with both actor and critic) for TD3.\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 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 TD3Policy.__init__ at 0x7f4bf45b0af0>",
|
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"_build": "<function TD3Policy._build at 0x7f4bf45b0b80>",
|
9 |
+
"_get_constructor_parameters": "<function TD3Policy._get_constructor_parameters at 0x7f4bf45b0c10>",
|
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"make_actor": "<function TD3Policy.make_actor at 0x7f4bf45b0ca0>",
|
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"make_critic": "<function TD3Policy.make_critic at 0x7f4bf45b0d30>",
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"forward": "<function TD3Policy.forward at 0x7f4bf45b0dc0>",
|
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"_predict": "<function TD3Policy._predict at 0x7f4bf45b0e50>",
|
14 |
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"set_training_mode": "<function TD3Policy.set_training_mode at 0x7f4bf45b0ee0>",
|
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"__abstractmethods__": "frozenset()",
|
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"_abc_impl": "<_abc._abc_data object at 0x7f4bf4a38ec0>"
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},
|
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"verbose": 1,
|
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"policy_kwargs": {
|
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"n_critics": 1
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},
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"observation_space": {
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23 |
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":type:": "<class 'gym.spaces.box.Box'>",
|
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ddpg-Ant-v3/policy.pth
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ddpg-Ant-v3/pytorch_variables.pth
ADDED
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ADDED
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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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|
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- Gym: 0.21.0
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env_kwargs.yml
ADDED
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{}
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replay.mp4
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ADDED
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{"mean_reward": 248.8030937, "std_reward": 287.0074907796492, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-28T18:15:50.112249"}
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