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.gitattributes

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README.md

@@ -0,0 +1,95 @@
+---
+library_name: stable-baselines3
+tags:
+- CarRacing-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: RecurrentPPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: CarRacing-v2
+      type: CarRacing-v2
+    metrics:
+    - type: mean_reward
+      value: 358.08 +/- 197.12
+      name: mean_reward
+      verified: false
+---
+
+# **RecurrentPPO** Agent playing **CarRacing-v2**
+This is a trained model of a **RecurrentPPO** agent playing **CarRacing-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
+and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
+
+The RL Zoo is a training framework for Stable Baselines3
+reinforcement learning agents,
+with hyperparameter optimization and pre-trained agents included.
+
+## Usage (with SB3 RL Zoo)
+
+RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
+SB3: https://github.com/DLR-RM/stable-baselines3<br/>
+SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
+
+Install the RL Zoo (with SB3 and SB3-Contrib):
+```bash
+pip install rl_zoo3
+```
+
+```
+# Download model and save it into the logs/ folder
+python -m rl_zoo3.load_from_hub --algo ppo_lstm --env CarRacing-v2 -orga Emperor-WS -f logs/
+python -m rl_zoo3.enjoy --algo ppo_lstm --env CarRacing-v2  -f logs/
+```
+
+If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
+```
+python -m rl_zoo3.load_from_hub --algo ppo_lstm --env CarRacing-v2 -orga Emperor-WS -f logs/
+python -m rl_zoo3.enjoy --algo ppo_lstm --env CarRacing-v2  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo ppo_lstm --env CarRacing-v2 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo ppo_lstm --env CarRacing-v2 -f logs/ -orga Emperor-WS
+```
+
+## Hyperparameters
+```python
+OrderedDict([('batch_size', 128),
+             ('clip_range', 0.2),
+             ('ent_coef', 0.0),
+             ('env_wrapper',
+              [{'gymnasium.wrappers.resize_observation.ResizeObservation': {'shape': 64}},
+               {'gymnasium.wrappers.gray_scale_observation.GrayScaleObservation': {'keep_dim': True}}]),
+             ('frame_stack', 2),
+             ('gae_lambda', 0.95),
+             ('gamma', 0.99),
+             ('learning_rate', 'lin_1e-4'),
+             ('max_grad_norm', 0.5),
+             ('n_envs', 8),
+             ('n_epochs', 4),
+             ('n_steps', 512),
+             ('n_timesteps', 4000000.0),
+             ('normalize', "{'norm_obs': False, 'norm_reward': True}"),
+             ('policy', 'CnnLstmPolicy'),
+             ('policy_kwargs',
+              'dict(log_std_init=-2, ortho_init=False, '
+              'enable_critic_lstm=False, activation_fn=nn.GELU, '
+              'lstm_hidden_size=128, )'),
+             ('sde_sample_freq', 4),
+             ('use_sde', True),
+             ('vf_coef', 0.5),
+             ('normalize_kwargs', {'norm_obs': False, 'norm_reward': False})])
+```
+
+# Environment Arguments
+```python
+{'render_mode': 'rgb_array'}
+```

args.yml

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+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - ppo_lstm
+  - - conf_file
+    - null
+  - - device
+    - auto
+  - - env
+    - CarRacing-v2
+  - - env_kwargs
+    - null
+  - - eval_env_kwargs
+    - null
+  - - eval_episodes
+    - 5
+  - - eval_freq
+    - 25000
+  - - gym_packages
+    - []
+  - - hyperparams
+    - n_epochs: 4
+  - - log_folder
+    - logs/
+  - - log_interval
+    - -1
+  - - max_total_trials
+    - null
+  - - n_eval_envs
+    - 1
+  - - n_evaluations
+    - null
+  - - n_jobs
+    - 1
+  - - n_startup_trials
+    - 10
+  - - n_timesteps
+    - 500000
+  - - n_trials
+    - 500
+  - - no_optim_plots
+    - false
+  - - num_threads
+    - -1
+  - - optimization_log_path
+    - null
+  - - optimize_hyperparameters
+    - false
+  - - progress
+    - false
+  - - pruner
+    - median
+  - - sampler
+    - tpe
+  - - save_freq
+    - -1
+  - - save_replay_buffer
+    - false
+  - - seed
+    - 1258192766
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - ''
+  - - track
+    - false
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1
+  - - wandb_entity
+    - null
+  - - wandb_project_name
+    - sb3
+  - - wandb_tags
+    - []

config.yml

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+!!python/object/apply:collections.OrderedDict
+- - - batch_size
+    - 128
+  - - clip_range
+    - 0.2
+  - - ent_coef
+    - 0.0
+  - - env_wrapper
+    - - gymnasium.wrappers.resize_observation.ResizeObservation:
+          shape: 64
+      - gymnasium.wrappers.gray_scale_observation.GrayScaleObservation:
+          keep_dim: true
+  - - frame_stack
+    - 2
+  - - gae_lambda
+    - 0.95
+  - - gamma
+    - 0.99
+  - - learning_rate
+    - lin_1e-4
+  - - max_grad_norm
+    - 0.5
+  - - n_envs
+    - 8
+  - - n_epochs
+    - 4
+  - - n_steps
+    - 512
+  - - n_timesteps
+    - 4000000.0
+  - - normalize
+    - '{''norm_obs'': False, ''norm_reward'': True}'
+  - - policy
+    - CnnLstmPolicy
+  - - policy_kwargs
+    - dict(log_std_init=-2, ortho_init=False, enable_critic_lstm=False, activation_fn=nn.GELU,
+      lstm_hidden_size=128, )
+  - - sde_sample_freq
+    - 4
+  - - use_sde
+    - true
+  - - vf_coef
+    - 0.5

env_kwargs.yml

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+render_mode: rgb_array

ppo_lstm-CarRacing-v2.zip

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ppo_lstm-CarRacing-v2/_stable_baselines3_version

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+2.3.0a2

ppo_lstm-CarRacing-v2/policy.optimizer.pth

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ppo_lstm-CarRacing-v2/policy.pth

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ppo_lstm-CarRacing-v2/pytorch_variables.pth

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ppo_lstm-CarRacing-v2/system_info.txt

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+- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.3.0a2
+- PyTorch: 2.1.0+cu121
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.29.1
+- OpenAI Gym: 0.26.2

replay.mp4

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results.json

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+{"mean_reward": 358.079023, "std_reward": 197.11957502963637, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-02-25T16:58:41.019049"}

train_eval_metrics.zip

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vec_normalize.pkl

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