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README.md +54 -7
args.yml +1 -1
dqn-LunarLander-v2.zip +2 -2
dqn-LunarLander-v2/data +63 -58
dqn-LunarLander-v2/policy.optimizer.pth +2 -2
dqn-LunarLander-v2/policy.pth +2 -2
dqn-LunarLander-v2/system_info.txt +3 -3
replay.mp4 +2 -2
results.json +1 -1
train_eval_metrics.zip +2 -2

README.md CHANGED Viewed

@@ -16,22 +16,69 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 41.68 +/- 30.81
       name: mean_reward
       verified: false
 ---
 # **DQN** Agent playing **LunarLander-v2**
 This is a trained model of a **DQN** agent playing **LunarLander-v2**
-using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
-## Usage (with Stable-baselines3)
-TODO: Add your code
 ```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
-...
 ```

       type: LunarLander-v2
     metrics:
     - type: mean_reward
+      value: -638.18 +/- 102.53
       name: mean_reward
       verified: false
 ---
 # **DQN** Agent playing **LunarLander-v2**
 This is a trained model of a **DQN** agent playing **LunarLander-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 dqn --env LunarLander-v2 -orga nsanghi -f logs/
+python -m rl_zoo3.enjoy --algo dqn --env LunarLander-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 dqn --env LunarLander-v2 -orga nsanghi -f logs/
+python -m rl_zoo3.enjoy --algo dqn --env LunarLander-v2  -f logs/
+```
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo dqn --env LunarLander-v2 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo dqn --env LunarLander-v2 -f logs/ -orga nsanghi
+```
+## Hyperparameters
 ```python
+OrderedDict([('batch_size', 128),
+             ('buffer_size', 50000),
+             ('exploration_final_eps', 0.1),
+             ('exploration_fraction', 0.12),
+             ('gamma', 0.99),
+             ('gradient_steps', -1),
+             ('learning_rate', 0.00063),
+             ('learning_starts', 0),
+             ('n_timesteps', 100000.0),
+             ('policy', 'MlpPolicy'),
+             ('policy_kwargs', 'dict(net_arch=[256, 256])'),
+             ('target_update_interval', 250),
+             ('train_freq', 4),
+             ('normalize', False)])
+```
+# Environment Arguments
+```python
+{'render_mode': 'rgb_array'}
 ```

args.yml CHANGED Viewed

@@ -54,7 +54,7 @@
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     - false
   - - seed
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     - null
   - - study_name

   - - save_replay_buffer
     - false
   - - seed
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   - - storage
     - null
   - - study_name

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-- OS: Linux-6.2.0-1019-azure-x86_64-with-glibc2.36 # 19~22.04.1-Ubuntu SMP Wed Jan 10 22:57:03 UTC 2024
 - Python: 3.9.18
 - Stable-Baselines3: 2.1.0
-- PyTorch: 2.2.1+cpu
 - GPU Enabled: False
-- Numpy: 1.26.4
 - Cloudpickle: 3.0.0
 - Gymnasium: 0.29.1
 - OpenAI Gym: 0.26.2

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 - Python: 3.9.18
 - Stable-Baselines3: 2.1.0
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 - GPU Enabled: False
+- Numpy: 1.26.1
 - Cloudpickle: 3.0.0
 - Gymnasium: 0.29.1
 - OpenAI Gym: 0.26.2

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