tags: | |
- FrozenLake-v1-4x4-no_slippery | |
- 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-no_slippery | |
type: FrozenLake-v1-4x4-no_slippery | |
metrics: | |
- type: mean_reward | |
value: 1.00 +/- 0.00 | |
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="friendlyGiraffe/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"]) | |
``` | |