• In this post, Iâll introduce a reinforcement learning (RL) algorithm based on an âalternativeâ paradigm: divide and conquer. • Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalability challenges), and scales well to long-horizon tasks. • We can do Reinforcement Learning (RL) based on divide and conquer, instead of temporal difference (TD) learning. • Problem setting: off-policy RL Our problem setting is off-policy RL. • Letâs briefly review what this means. • There are two classes of algorithms in RL: on-policy RL and off-policy RL.

Article Summaries:

  • In this post, Iâll introduce a reinforcement learning (RL) algorithm based on an âalternativeâ paradigm: divide and conquer. Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalability challenges), and scales well to long-horizon tasks. We can do Reinforcement Learning (RL) based on divide and conquer, instead of temporal difference (TD) learning. Problem setting: off-policy RL Our problem setting is off-policy RL. Letâs briefly review what this means. There are two classes of algorithms in RL: on-policy RL and off-policy RL. On-policy RL

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