reinforcement-learning_skill

This skill enables you to design, implement, and compare reinforcement learning algorithms across Q-learning, DQN, PPO, and multi-agent setups.

Python

5

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

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Installation

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npx veilstrat add skill pluginagentmarketplace/custom-plugin-ai-data-scientist --skill reinforcement-learning

SKILL.md16.1 KB

Overview

This skill provides a practical toolkit for building, training, and evaluating reinforcement learning agents using methods from tabular Q-learning to modern policy-gradient algorithms like PPO and actor-critic. It includes working patterns for Deep Q-Networks, REINFORCE, PPO, multi-agent setups, reward shaping, curriculum learning, and production-ready usage with Stable Baselines3. The focus is on actionable code patterns and components you can plug into Gym-compatible environments.

How this skill works

The skill supplies reference implementations for core RL components: a tabular Q-learning agent, a DQN with replay buffer and target network, policy-gradient agents (REINFORCE) and an actor-critic PPO implementation with GAE. It also shows multi-agent wrappers, reward shaping utilities, curriculum scheduling, and examples of vectorized training using Stable Baselines3. Use these components to interact with Gym environments, collect transitions, compute returns/advantages, and perform policy or value updates.

When to use it

Training agents in Gym or custom simulation environments
Prototyping RL baselines (Q-learning, DQN, REINFORCE, PPO)
Building multi-agent systems where agents learn independently
Applying reward shaping or curriculum learning to speed up training
Scaling experiments toward production with Stable Baselines3 and vectorized envs

Best practices

Start simple: validate on small, well-known environments (CartPole, FrozenLake) before scaling
Normalize inputs and returns where appropriate to stabilize training
Use replay buffers and target networks for off-policy learners like DQN
Monitor and anneal exploration (epsilon) and regularly evaluate on held-out episodes
Clip gradients and use entropy bonuses for policy stability; tune GAE and clip_ratio for PPO

Example use cases

Train a DQN to play discrete-action control tasks (CartPole, Atari prototypes) using experience replay and target updates
Use PPO with actor-critic networks and GAE for continuous or complex policies in robotics simulations
Apply REINFORCE as a simple policy-gradient baseline to validate learning and reward shaping ideas
Set up independent learners in multi-agent simulations for decentralized coordination experiments
Use Stable Baselines3 with vectorized envs and eval callbacks to scale training and save best models

FAQ

Start with a basic DQN for discrete tasks and PPO for continuous or high-dimensional problems; use tabular Q-learning only for small discrete state spaces.

How do I stabilize training?

Normalize observations and returns, use replay buffers and target networks for DQN, apply entropy regularization and gradient clipping for policy methods, and tune learning rate, batch size, and GAE parameters.