| tags: | |
| - FrozenLake-v1-4x4 | |
| - q-learning | |
| - reinforcement-learning | |
| - custom-implementation | |
| model-index: | |
| - name: q-FrozenLake-v1-4x4-noSlippery | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: FrozenLake-v1-4x4 | |
| type: FrozenLake-v1-4x4 | |
| metrics: | |
| - type: mean_reward | |
| value: 0.76 +/- 0.43 | |
| name: mean_reward | |
| verified: false | |
| # **Q-Learning** Agent playing **FrozenLake-v1** | |
| This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . | |
| ## Usage | |
| ```python | |
| model = load_from_hub(repo_id="joelearn22/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") | |
| # Don't forget to check if you need to add additional attributes (is_slippery=False etc) | |
| env = gym.make(model["env_id"]) | |
| evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) | |
| ``` | |