DRL-Pytorch Repo Skill

Use this skill when a task involves the XinJingHao DRL-Pytorch repository or its standalone PyTorch reinforcement-learning scripts: Q-learning, DQN/DDQN/Dueling DQN, prioritized replay, C51, NoisyNet, PPO, DDPG, TD3, SAC, Atari NoFrameskip DQN, or Actor-Sharer-Learner.

Do not use this skill for Stable-Baselines3, CleanRL, Tianshou, PettingZoo, RLHF packages, or generic RL theory unless the task explicitly needs DRL-Pytorch command/API details.

First checks

1. Read repository provenance if you need to decide whether this skill matches a current checkout or should be refreshed.
2. Read quickstart and environment before installing extras, running smoke checks, or starting training.
3. Use algorithm index to route by algorithm name, action space, EnvIdex, and checkpoint pattern.
4. Use cross-cutting troubleshooting for CUDA defaults, optional Gymnasium extras, colliding module imports, checkpoints, TensorBoard, or stochastic training failures.

Choose the sub-skill

Request signal	Read
Q-learning, CliffWalking, DQN, DDQN, Dueling, PER, prioritized replay, C51, NoisyNet, CartPole, LunarLander discrete	value-based-discrete-control
PPO, DDPG, TD3, SAC, actor-critic, policy gradient, Pendulum, LunarLanderContinuous, Humanoid, HalfCheetah, BipedalWalker, continuous control	policy-and-actor-critic-control
Atari, Pong, Enduro, NoFrameskip, AtariNames, ALE ROMs, OpenCV wrappers, EnvPool, Actor-Sharer-Learner, ASL multiprocessing	atari-and-asl-workflows

If a task spans multiple algorithms, start with the root algorithm index, then read each owning sub-skill. Keep value-based non-Atari DQN separate from Atari DQN: both use DQN terminology but have different environments, wrappers, dependencies, and checkpoints.

Repository operating model

DRL-Pytorch is not an installable Python distribution. It is a script collection where each algorithm directory has its own main.py, local implementation module, and often a colliding utils.py. Commands that run original launchers must be executed from the selected algorithm directory in the user's DRL-Pytorch checkout. Bundled scripts in this skill accept --repo-root so diagnostics do not depend on the checkout used to create this skill.

Baseline CPU-safe dependencies for modern workflows are Python 3.11, Gymnasium 0.29.x, NumPy 1.26.x, PyTorch 2.1.x, TensorBoard for --write True, and Matplotlib for C51 render helpers. Optional extras include Box2D, MuJoCo, Atari ALE/ROMs/OpenCV, EnvPool, and CUDA acceleration.

Safe diagnostics

Use the bundled algorithm matrix without a checkout:

python scripts/drl_pytorch_algorithm_matrix.py --format table

Use no-training diagnostics when a DRL-Pytorch checkout is available:

python scripts/drl_pytorch_safe_smoke.py --repo-root <DRL-Pytorch-checkout> --suite all

For narrower checks:

python scripts/drl_pytorch_safe_smoke.py --repo-root <DRL-Pytorch-checkout> --suite value
python scripts/drl_pytorch_safe_smoke.py --repo-root <DRL-Pytorch-checkout> --suite policy
python scripts/drl_pytorch_safe_smoke.py --repo-root <DRL-Pytorch-checkout> --suite atari

The smoke helpers import modules and run tiny CPU object/network checks. They do not train, render, download Atari ROMs, create optional Box2D/MuJoCo/Atari environments, write checkpoints, launch TensorBoard, or start EnvPool workers.

Training guardrails

• Set --dvc cpu or --device cpu for CPU-only checks; several launchers default to CUDA.
• Prefer --Max_train_steps 0 --write False --render False before any real training when the launcher supports it.
• Install Box2D only for LunarLander/BipedalWalker, MuJoCo only for Humanoid/HalfCheetah, Atari extras/ROMs/OpenCV only for Atari NoFrameskip, and EnvPool only for ASL.
• Treat full training, rendering, checkpoint playback, Atari environment creation, and ASL multiprocessing as user-approved side-effectful work.
• Check checkpoint filename conventions before --Loadmodel True; binaries are not bundled in this skill.

If editing or refreshing

When repository code changes, refresh from source evidence rather than patching this skill by memory. Pay special attention to launcher flags, EnvIdex maps, class names, checkpoint naming, optional dependency gates, and repeated local module names.