wangshusen/DRL
DRL
DRL is a curated educational resource that teaches deep reinforcement learning through a structured series of lectures and videos. It covers the three main families of reinforcement learning methods: actor-critic architectures, value-based algorithms like Q-learning and DQN, and policy-based techniques that directly optimize an agent's action-selection strategy.
The curriculum extends beyond these core topics to include imitation learning, multi-agent training, and methods for handling continuous action spaces. Content is organized as markdown-driven documentation that generates static, navigable pages, creating a clear learning path through the mathematical foundations and practical algorithms of reinforcement learning.
Features
- Reinforcement Learning Curricula - Provides a structured educational resource teaching the mathematical foundations of reinforcement learning.
- Actor-Critic Architectures - Teaches actor-critic architectures that combine policy and value learning for stable training.
- Deep Learning Policy Frameworks - Explains policy gradient techniques that directly optimize an agent's action-selection strategy.
- Deep Q-Learning Frameworks - Teaches algorithms that learn action values to derive optimal policies through Q-learning and DQN.
- Policy Gradient Methods - Provides a structured tutorial series explaining policy gradient techniques for directly optimizing action-selection strategies.
- Reinforcement Learning Value Estimators - Teaches algorithms like Q-learning and DQN that learn action values to derive optimal policies.
- Value-Based Methods Tutorials - Ships a dedicated curriculum covering Q-learning and DQN algorithms for learning action values.
- Lecture Sequences - Organizes educational content as a structured sequence of lectures and videos for progressive learning.
- Reinforcement Learning Theory - Teaches the mathematical foundations and algorithms of reinforcement learning through structured lectures.
- Continuous Action Space Tutorials - Teaches how to handle environments with real-valued actions by extending policy gradient methods.
- Expert Imitation Learning - Teaches imitation learning approaches that copy expert behavior instead of learning from trial and error.
- Multi-Agent Training - Addresses the unique challenges of environments where several learning agents interact simultaneously.
