Quentin Gallouédec
commited on
Commit
•
b1aa77e
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Parent(s):
2c98502
Initial commit
Browse files- .gitattributes +1 -0
- README.md +77 -0
- args.yml +83 -0
- config.yml +23 -0
- env_kwargs.yml +1 -0
- ppo-CartPole-v1.zip +3 -0
- ppo-CartPole-v1/_stable_baselines3_version +1 -0
- ppo-CartPole-v1/data +95 -0
- ppo-CartPole-v1/policy.optimizer.pth +3 -0
- ppo-CartPole-v1/policy.pth +3 -0
- ppo-CartPole-v1/pytorch_variables.pth +3 -0
- ppo-CartPole-v1/system_info.txt +7 -0
- replay.mp4 +3 -0
- results.json +1 -0
- train_eval_metrics.zip +3 -0
.gitattributes
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@@ -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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- CartPole-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: PPO
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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: CartPole-v1
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type: CartPole-v1
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metrics:
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- type: mean_reward
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value: 500.00 +/- 0.00
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name: mean_reward
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verified: false
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---
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# **PPO** Agent playing **CartPole-v1**
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This is a trained model of a **PPO** agent playing **CartPole-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 ppo --env CartPole-v1 -orga qgallouedec -f logs/
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python -m rl_zoo3.enjoy --algo ppo --env CartPole-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 ppo --env CartPole-v1 -orga qgallouedec -f logs/
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python -m rl_zoo3.enjoy --algo ppo --env CartPole-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 ppo --env CartPole-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 ppo --env CartPole-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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('clip_range', 'lin_0.2'),
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('ent_coef', 0.0),
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('gae_lambda', 0.8),
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('gamma', 0.98),
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('learning_rate', 'lin_0.001'),
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('n_envs', 8),
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('n_epochs', 20),
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('n_steps', 32),
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('n_timesteps', 100000.0),
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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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- ppo
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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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- CartPole-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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- 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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+
- 2542014453
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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/CartPole-v1__ppo__2542014453__1675782355
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- - track
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+
- true
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66 |
+
- - 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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- - - batch_size
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- 256
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4 |
+
- - clip_range
|
5 |
+
- lin_0.2
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6 |
+
- - ent_coef
|
7 |
+
- 0.0
|
8 |
+
- - gae_lambda
|
9 |
+
- 0.8
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10 |
+
- - gamma
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- 0.98
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+
- - learning_rate
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+
- lin_0.001
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+
- - n_envs
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- 8
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- - n_epochs
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- 20
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- - n_steps
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- 32
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- - n_timesteps
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- 100000.0
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- - policy
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- MlpPolicy
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env_kwargs.yml
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{}
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ppo-CartPole-v1.zip
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version https://git-lfs.github.com/spec/v1
|
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oid sha256:8cf9bd2598aa14572ff38a745365b3450251f027dfa114032acf7b0029f60783
|
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+
size 143001
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ppo-CartPole-v1/_stable_baselines3_version
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1.8.0a6
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ppo-CartPole-v1/data
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{
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"policy_class": {
|
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":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
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+
"__module__": "stable_baselines3.common.policies",
|
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+
"__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 ",
|
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+
"__init__": "<function ActorCriticPolicy.__init__ at 0x7f1c806d3ee0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1c806d3f70>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1c806d4040>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1c806d40d0>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f1c806d4160>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f1c806d41f0>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f1c806d4280>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1c806d4310>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f1c806d43a0>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1c806d4430>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1c806d44c0>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1c806d4550>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f1c806d5400>"
|
21 |
+
},
|
22 |
+
"verbose": 1,
|
23 |
+
"policy_kwargs": {},
|
24 |
+
"observation_space": {
|
25 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
26 |
+
":serialized:": "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",
|
27 |
+
"dtype": "float32",
|
28 |
+
"_shape": [
|
29 |
+
4
|
30 |
+
],
|
31 |
+
"low": "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]",
|
32 |
+
"high": "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]",
|
33 |
+
"bounded_below": "[ True True True True]",
|
34 |
+
"bounded_above": "[ True True True True]",
|
35 |
+
"_np_random": null
|
36 |
+
},
|
37 |
+
"action_space": {
|
38 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
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ppo-CartPole-v1/policy.optimizer.pth
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