metadata
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3_v0_DeepRLCourse
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
This is a trained model of a Q-Learning agent playing FrozenLake-v1 with no slippery. It leverages the Gymnasium environment, useful to get acquainted with Q-Learning. Very easy but useful to understand the basic concepts.
Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3. It leverages the Gymnasium environment, useful to get acquainted with Q-Learning. Very easy but useful to understand the basic concepts.
Usage
import gymnasium as gym
from huggingface_sb3 import load_from_hub
model = load_from_hub(repo_id="Mattizza/Taxi-v3_v0_DeepRLCourse", filename="q-learning.pkl")
env = gym.make(model["env_id"])