RL without TD learning
BAIR
Researchers introduced Transitive RL, a reinforcement learning algorithm based on divide-and-conquer instead of temporal difference learning, designed to address scalability challenges in off-policy RL for long-horizon tasks. The method reduces Bellman recursions logarithmically by recursively splitting trajectories and uses expectile regression to select intermediate subgoals from the dataset. In experiments on OGBench benchmarks with tasks requiring up to 3,000 environment steps, Transitive RL matched or exceeded strong baselines including optimally-tuned n-step TD learning without requiring manual hyperparameter selection.
Why it matters
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-policy RL means we can only use fresh data collected by the current policy. In other words, we have to throw away old data each time we update the policy. Algorithms like PPO and GRPO (and policy gradient methods in general) belong to this category. Off-policy RL means we don’t have this restriction: we can use any kind of data, including old experience, human demonstrations, Internet data, and so on. So o