Serendipity
e8b51240f9
实现完整的DQN算法框架，用于Atari Space Invaders游戏训练。包括： - QNetwork和DuelingQNetwork神经网络架构 - 经验回放缓冲区（标准和优先级版本） - DQN智能体实现ε-greedy策略和Double DQN - 环境包装器（灰度化、调整大小、帧堆叠等） - 训练器、评估脚本和图表生成工具 - 详细的项目文档和依赖配置
PPO for CarRacing-v3
From-scratch PPO implementation for CarRacing-v3. No Stable-Baselines or other RL libraries used.
Setup
conda activate my_env
uv pip install -r requirements.txt
Train
python train.py --steps 500000
Evaluate
python src/evaluate.py --model models/ppo_carracing_final.pt --episodes 10
TensorBoard
tensorboard --logdir logs/tensorboard
Project Structure
src/
├── network.py # Actor (Gaussian policy) and Critic (Value) networks
├── replay_buffer.py # Rollout buffer with GAE computation
├── trainer.py # PPO update with clipped surrogate objective
├── utils.py # Environment wrappers (grayscale, resize, frame stack)
└── evaluate.py # Evaluation script
train.py # Main training entry point
models/ # Saved checkpoints
logs/tensorboard/ # TensorBoard logs
Hyperparameters
| Parameter | Value |
|---|---|
| Learning rate | 3e-4 |
| Gamma | 0.99 |
| GAE lambda | 0.95 |
| Clip epsilon | 0.2 |
| PPO epochs | 4 |
| Mini-batch size | 64 |
| Rollout steps | 2048 |
| Entropy coefficient | 0.01 |
| Value coefficient | 0.5 |
| Max gradient norm | 0.5 |