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Songyao86/
blackjack-qlearning-agent

reinforcement-learning

Blackjack-v1

q-learning

custom-implementation

model-index

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Q-Learning Agent playing Blackjack-v1

Training Parameters

Environment ID: Blackjack-v1
Training Episodes: 10000
Max Steps per Episode: 99
Learning Rate: 0.7
Gamma (Discount Factor): 0.95

Evaluation Results

Mean Reward: -0.19 ± 0.95
Evaluation Episodes: 100

Usage

python

from huggingface_hub import hf_hub_download
import pickle
import gymnasium as gym
import numpy as np

# 请将下面的占位符替换为你的实际仓库信息
repo_id = "YOUR_USERNAME/YOUR_REPO_NAME"  # 替换为你的仓库
filename = "q-learning.pkl"

# 加载模型
model_path = hf_hub_download(repo_id=repo_id, filename=filename)

with open(model_path, "rb") as f:
    model = pickle.load(f)

# 重建环境
env = gym.make(
    model["env_id"],
    render_mode="rgb_array",
    **model.get("env_config", {})
)

# 使用Q表进行推理
qtable = model["qtable"]

# 简单的推理示例
state = env.reset()
terminated = False
while not terminated:
# 状态转换为索引
if isinstance(state, tuple):
    state_idx = model.get("state_to_index", lambda s: s)(state)
else:
    state_idx = state

action = np.argmax(qtable[state_idx])
state, reward, terminated, truncated, _ = env.step(action)

Songyao86/blackjack-qlearning-agent

Q-Learning Agent playing Blackjack-v1

Training Parameters

Evaluation Results

Usage

Songyao86/
blackjack-qlearning-agent