arXiv:1906.10025
공식 코드 · Papers with Code
Modern Deep Reinforcement Learning Algorithms
원문 초록
Recent advances in Reinforcement Learning, grounded on combining classical theoretical results with Deep Learning paradigm, led to breakthroughs in many artificial intelligence tasks and gave birth to Deep Reinforcement Learning (DRL) as a field of research. In this work latest DRL algorithms are reviewed with a focus on their theoretical justification, practical limitations and observed empirical properties.
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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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