6001k1d commited on
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README.md CHANGED
@@ -8,16 +8,17 @@ tags:
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  model-index:
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  - name: DQN
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  results:
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- - metrics:
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- - type: mean_reward
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- value: 599.00 +/- 222.51
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- name: mean_reward
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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: SpaceInvadersNoFrameskip-v4
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  type: SpaceInvadersNoFrameskip-v4
 
 
 
 
 
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  ---
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  # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
@@ -35,17 +36,28 @@ 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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  ```
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  # Download model and save it into the logs/ folder
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- python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga 6001k1d -f logs/
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- python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
 
 
 
 
 
 
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  ```
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  ## Training (with the RL Zoo)
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  ```
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- python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
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  # Upload the model and generate video (when possible)
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- python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga 6001k1d
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  ```
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  ## Hyperparameters
@@ -58,7 +70,7 @@ OrderedDict([('batch_size', 32),
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  ('exploration_fraction', 0.1),
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  ('frame_stack', 4),
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  ('gradient_steps', 1),
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- ('learning_rate', 0.0001),
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  ('learning_starts', 100000),
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  ('n_timesteps', 1000000.0),
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  ('optimize_memory_usage', False),
@@ -67,3 +79,8 @@ OrderedDict([('batch_size', 32),
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  ('train_freq', 4),
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  ('normalize', False)])
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  ```
 
 
 
 
 
 
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  model-index:
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  - name: DQN
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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: SpaceInvadersNoFrameskip-v4
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  type: SpaceInvadersNoFrameskip-v4
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+ metrics:
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+ - type: mean_reward
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+ value: 443.50 +/- 142.69
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+ name: mean_reward
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+ verified: false
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  ---
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  # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
 
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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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  ```
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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 dqn --env SpaceInvadersNoFrameskip-v4 -orga 6001k1d -f logs/
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+ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
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+ ```
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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 dqn --env SpaceInvadersNoFrameskip-v4 -orga 6001k1d -f logs/
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+ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga 6001k1d
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  ```
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  ## Hyperparameters
 
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  ('exploration_fraction', 0.1),
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  ('frame_stack', 4),
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  ('gradient_steps', 1),
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+ ('learning_rate', 0.0005),
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  ('learning_starts', 100000),
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  ('n_timesteps', 1000000.0),
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  ('optimize_memory_usage', False),
 
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  ('train_freq', 4),
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  ('normalize', False)])
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  ```
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+
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+ # Environment Arguments
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+ ```python
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+ {'render_mode': 'rgb_array'}
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+ ```
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