SVQN: Sequential Variational Soft Q-Learning Networks

ICLR 2020 Shiyu HuangHang SuJun ZhuTing Chen

Partially Observable Markov Decision Processes (POMDPs) are popular and flexible models for real-world decision-making applications that demand the information from past observations to make optimal decisions. Standard reinforcement learning algorithms for solving Markov Decision Processes (MDP) tasks are not applicable, as they cannot infer the unobserved states... (read more)

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