wangshusen/DRL

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.

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