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) lear...
TRL represents an advancement in off-policy RL by utilizing a recursive structure that decomposes complex tasks into simpler steps, allowing agents to learn more efficiently from off-policy data. However, the algorithm still faces challenges when dealing with stochastic environments and requires further improvement in stability and simplicity. The potential applications of TRL extend beyond goal-conditioned reinforcement learning tasks, hinting at its possible utility in reward-based RL problems...