arXiv:2201.09746
공식 코드 · Papers with Code
Reinforcement Learning Textbook
원문 초록
This textbook covers principles behind main modern deep reinforcement learning algorithms that achieved breakthrough results in many domains from game AI to robotics. All required theory is explained with proofs using unified notation and emphasize on the differences between different types of algorithms and the reasons why they are constructed the way they are.
함께 살펴볼 논문
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Deep Reinforcement Learning with Gradient Eligibility Traces
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and Constraints
Task Independent Capsule-Based Agents for Deep Q-Learning
A State Representation Dueling Network for Deep Reinforcement Learning
Drafting in Collectible Card Games via Reinforcement Learning
토론
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