Does it support Offline RL where I only have a fixed dataset?

Yes, it specifically identifies offline data regimes and routes to conservative algorithms designed to handle distribution shift and bootstrapping errors common in fixed datasets.

Is this suitable for multi-agent environments?

Absolutely. It handles multi-agent scenarios (MARL), providing strategies for cooperation, competition, and credit assignment in shared environments.

How does this skill help when my agent isn't learning?

It provides a dedicated debugging framework to identify common issues such as improper reward scaling, lack of exploration, or network architecture mismatches before suggesting algorithm changes.

Can I use this skill for continuous control in robotics?

Yes, it includes specialized routing for continuous action spaces, recommending actor-critic methods like SAC which are highly sample-efficient for robotics.

What reinforcement learning algorithms does this skill cover?

The skill provides guidance for a wide range of algorithms including DQN, PPO, Soft Actor-Critic (SAC), TD3, and specialized Offline-RL algorithms like CQL and IQL.

Deep Reinforcement Learning Meta-Skill

Name: Deep Reinforcement Learning Meta-Skill
Author: tachyon-beep

bytachyon-beep

Ciencia de Datos y ML

Routes users to the most effective reinforcement learning algorithms and implementation patterns based on specific problem characteristics.

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This specialized meta-skill acts as the central intelligence hub for implementing Deep Reinforcement Learning (RL) within Claude. It systematically analyzes problem parameters—such as discrete versus continuous action spaces, online versus offline data availability, and sample efficiency requirements—to guide developers through a suite of 12 specialized RL sub-skills. Whether you are building agents for robotics control, optimizing game strategies, or debugging agent convergence issues, this skill ensures you apply the correct theoretical framework, from Bellman equations to advanced actor-critic architectures.

Características Principales

Intelligent routing to 12 specialized Deep RL sub-skills
Advanced multi-agent (MARL) and model-based RL strategy selection
0 GitHub stars
Support for both online interaction and Offline RL strategies
Structured debugging protocols for non-converging training loops
Diagnostic framework for classifying action spaces and data regimes

Casos de Uso

Developing AI for complex games and sequential decision-making environments
Implementing Conservative Q-Learning (CQL) for training on fixed, logged datasets
Designing autonomous agents for robotics manipulation or continuous control systems

Acerca de

Características Principales

Intelligent routing to 12 specialized Deep RL sub-skills
Advanced multi-agent (MARL) and model-based RL strategy selection
0 GitHub stars
Support for both online interaction and Offline RL strategies
Structured debugging protocols for non-converging training loops
Diagnostic framework for classifying action spaces and data regimes

Casos de Uso

Developing AI for complex games and sequential decision-making environments
Implementing Conservative Q-Learning (CQL) for training on fixed, logged datasets
Designing autonomous agents for robotics manipulation or continuous control systems