using-deep-rl

What problem does it solve?

This Skill acts as the entry point to the Deep RL pack, routing problems to the correct specialized RL skills based on problem type, data regime, and resource constraints.

Core Features & Use Cases

• Routes to the 12 specialized Deep RL skills based on problem characteristics (MDP, online/offline, continuous vs discrete actions, multi-agent)
• Provides reference sheets located in the same directory for quick lookup
• Helps you quickly identify whether to use foundations, value-based, policy-gradient, actor-critic, model-based, offline, MARL, exploration, reward shaping, debugging, environments, or evaluation
• Use case: You have a discrete action space RL problem with sparse rewards — the router directs you to value-based methods vs policy-gradient-methods based on problem framing.

Quick Start

Load this skill and ask for a routing decision: e.g., "I want to train an agent in a discrete action space with online learning; where should I begin?"