|
--- |
|
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> |
|
|
|
``` |
|
|
