RL without TD learning
Evolving story · 1 updatesReinforcement Learning Without Temporal Difference LearningTimeline →A UC Berkeley BAIR blog post proposes a reinforcement learning algorithm that replaces temporal difference (TD) learning with a divide-and-conquer paradigm, aiming to improve scalability for long-horizon tasks in off-policy RL settings.
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
Source: RL without TD learning. Read the full piece at the source.
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