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---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-non_slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
n_training_episodes = 200000 # Total training episodes <br>
learning_rate = 0.8 # Learning rate <br>
# Evaluation parameters
n_eval_episodes = 100 # Total number of test episodes <br>
# Environment parameters <br>
env_id = "FrozenLake-v1" # Name of the environment <br>
max_steps = 100 # Max steps per episode <br>
gamma = 0.99 # Discounting rate <br>
eval_seed = [] # The evaluation seed of the environment <br>
# Exploration parameters <br>
epsilon = 1.0 # Exploration rate <br>
max_epsilon = 1.0 # Exploration probability at start <br>
min_epsilon = 0.05 # Minimum exploration probability <br>
decay_rate = 0.00005 # Exponential decay rate for exploration prob <br>
```
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