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--- |
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tags: |
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- CartPole-v1 |
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- reinforce |
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- reinforcement-learning |
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- custom-implementation |
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- deep-rl-class |
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model-index: |
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- name: Reinforce-CartPole-v1 |
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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: 109.92 +/- 16.87 |
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name: mean_reward |
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verified: false |
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--- |
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# **Q-Learning** Agent playing **CartPole-v1** |
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This is a trained model of a **Reinforce** agent playing **CartPole-v1** . |
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## Usage |
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```python |
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model = load_from_hub(repo_id="sayby/Reinforce-CartPole-v1", filename="model.pt") |
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# Don't forget to check if you need to add additional attributes (is_slippery=False etc) |
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env = gym.make(model["env_id"]) |
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evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["eval_seed"]) |
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``` |
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