Does it support custom environments?

Yes, it includes specialized routing for environment setup using the Gym API, wrappers, and vectorization for parallel training.

How does the Deep RL Meta-Skill choose an algorithm?

It uses a diagnostic framework evaluating action spaces (discrete vs. continuous), data availability (online vs. offline), and specific requirements like multi-agent support.

Can this skill help if my agent isn't learning?

Yes, it routes to a dedicated debugging skill to identify issues with reward scaling, exploration, learning rates, and network architectures.

What RL algorithm families are covered?

The skill provides guidance for value-based methods (DQN), policy gradients (PPO), actor-critic models (SAC, TD3), model-based RL, and specialized areas like Offline RL.

Why use this meta-skill instead of a specific algorithm?

Reinforcement learning is sensitive to problem types; this skill ensures you don't use suboptimal algorithms (like DQN for continuous actions) and addresses the 'not learning' phase through systematic debugging.

Deep Reinforcement Learning (RL) Meta-Skill

Name: Deep Reinforcement Learning (RL) Meta-Skill
Author: tachyon-beep

bytachyon-beep

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Data Science & ML

Routes reinforcement learning problems to specialized algorithms and implementation strategies based on task characteristics and environmental constraints.

About

This skill acts as an intelligent entry point for deep reinforcement learning (RL) projects, guiding users through the selection and implementation of algorithms like DQN, PPO, and SAC. By analyzing specific problem variables—such as discrete versus continuous action spaces, online versus offline data regimes, and multi-agent requirements—it ensures the most effective RL framework is applied. Beyond algorithm selection, it provides specialized guidance for debugging non-convergent agents, designing reward functions, and configuring custom environments, making it an essential tool for robotics, game AI, and complex control systems.

Key Features

Intelligent routing to 12 specialized deep-RL algorithm modules
Decision framework for discrete and continuous action space classification
Strategic guidance for online, offline, and multi-agent learning scenarios
Expert debugging paths for common training issues like exploding gradients
Best practices for reward shaping and environment configuration
5 GitHub stars

Use Cases

Troubleshooting training failures and non-convergent reinforcement learning agents
Implementing complex autonomous agents for robotics or gaming environments
Selecting the optimal RL algorithm for custom simulation and control tasks

About

Key Features

Intelligent routing to 12 specialized deep-RL algorithm modules
Decision framework for discrete and continuous action space classification
Strategic guidance for online, offline, and multi-agent learning scenarios
Expert debugging paths for common training issues like exploding gradients
Best practices for reward shaping and environment configuration
5 GitHub stars

Use Cases

Troubleshooting training failures and non-convergent reinforcement learning agents
Implementing complex autonomous agents for robotics or gaming environments
Selecting the optimal RL algorithm for custom simulation and control tasks