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tags:
- reinforcement-learning
- q-learning
- gymnasium
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
type: FrozenLake-v1-4x4-no_slippery
name: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
name: Mean reward
value: 1.000000
- type: success_rate
name: Success rate
value: 100.000000
---
# Q-Learning Agent playing FrozenLake-v1-4x4-no_slippery
This repository contains a tabular Q-Learning agent trained on
**FrozenLake-v1-4x4-no_slippery** using Gymnasium.
The agent was implemented as part of the Hugging Face Deep
Reinforcement Learning Course.
## Evaluation results
| Metric | Result |
|---|---:|
| Evaluation episodes | 1000 |
| Mean reward | 1.0000 |
| Reward standard deviation | 0.0000 |
| Success rate | 100.00% |
| Mean episode length | 6.00 |
## Agent replay
## Repository files
- `q-learning.pkl`: Q-table, environment settings and hyperparameters
- `qtable.npy`: NumPy Q-table
- `results.json`: evaluation summary
- `evaluation_episodes.csv`: individual evaluation episodes
- `training_history.csv`: training history, when available
- `replay.gif`: animated agent replay
- `replay.mp4`: video replay, when available
## Load the trained agent
```python
import pickle
import gymnasium as gym
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(
repo_id="a1914114315/q-FrozenLake-v1-4x4-noSlippery",
filename="q-learning.pkl"
)
with open(model_path, "rb") as file:
model = pickle.load(file)
qtable = model["qtable"]
env = gym.make(
model["env_id"],
**model.get("env_kwargs", {})
)
print("Environment:", model["env_id"])
print("Q-table shape:", qtable.shape)
print(
"Mean reward:",
model["evaluation"]["mean_reward"]
)
```
## Training configuration
```json
{
"n_training_episodes": 10000,
"max_steps": 99,
"learning_rate": 0.7,
"gamma": 0.95,
"max_epsilon": 1.0,
"min_epsilon": 0.05,
"decay_rate": 0.0005,
"seed": 42
}
```
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