| | --- |
| | tags: |
| | - FrozenLake-v1-4x4-no_slippery |
| | - q-learning |
| | - reinforcement-learning |
| | - custom-implementation |
| | model-index: |
| | - name: q-FrozenLake-v1 |
| | results: |
| | - task: |
| | type: reinforcement-learning |
| | name: reinforcement-learning |
| | dataset: |
| | name: FrozenLake-v1-4x4-no_slippery |
| | type: FrozenLake-v1-4x4-no_slippery |
| | metrics: |
| | - type: mean_reward |
| | value: 1.11 +/- 0.00 |
| | |
| | name: mean_reward |
| | verified: false |
| | --- |
| | |
| | # **Q-Learning** Agent playing1 **FrozenLake-v1** |
| | This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | |
| | model = load_from_hub(repo_id="diskya/q-FrozenLake-v1", 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"]) |
| | ``` |
| | |
