|
--- |
|
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 playing1 **FrozenLake-v1** |
|
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . |
|
|
|
our Q-Learning agent is going **to navigate from the starting state (S) to the goal state (G) by walking only on frozen tiles (F) and avoid holes (H)**. |
|
|
|
|
|
## Usage |
|
|
|
```python |
|
|
|
model = load_from_hub(repo_id="InMDev/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"]) |
|
``` |
|
|
