RL without TD learning

In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” 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. Let’s briefly review what this means.

There are two classes of algorithms in RL: on-policy RL and off-policy RL. On-polic