SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation

ICML 2018 Bo DaiAlbert ShawLihong LiLin XiaoNiao HeZhen LiuJianshu ChenLe Song

When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades. The fundamental difficulty is that the Bellman operator may become an expansion in general, resulting in oscillating and even divergent behavior of popular algorithms like Q-learning... (read more)

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