12. Reinforcement Learning System

Reinforcement learning (RL) has emerged as a subfield of machine learning that focuses on how autonomous agents can learn to make optimal decisions through interactions with their environments. By designating rewards or penalties for each action taken, RL provides a framework for training intelligent systems to maximize cumulative reward over time.

• 12.1. Overview
• 12.2. Introduction to Reinforcement Learning
  • 12.2.1. Background
  • 12.2.2. Reinforcement Learning Components
  • 12.2.3. Markov Decision Process
• 12.3. Single-Node Reinforcement Learning System
  • 12.3.1. RL System
  • 12.3.2. RLzoo
  • 12.3.3. Other Systems
• 12.4. Distributed Reinforcement Learning System
  • 12.4.1. Distributed RL Algorithm–A3C
  • 12.4.2. Distributed RL Algorithm–IMPALA
  • 12.4.3. Other Algorithms
  • 12.4.4. Distributed RL System – RLlib
  • 12.4.5. Distributed RL System–Reverb and Acme
• 12.5. Multi-agent Reinforcement Learning
  • 12.5.1. Multi-agent RL
  • 12.5.2. Game Example
  • 12.5.3. Self-play
• 12.6. Multi-agent Reinforcement Learning System
  • 12.6.1. Curse of Multi-agent
  • 12.6.2. Complex Game Types
  • 12.6.3. Algorithm Heterogeneity
  • 12.6.4. Hybrid Methods
• 12.7. Chapter Summary