arXiv:2509. 06810v3 Announce Type: replace-cross Abstract: Humans can uniquely assign value to novel, abstract outcomes to support reinforcement learning.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2608.30406v1 Announce Type: new Abstract: Goal-conditioned reinforcement learning struggles with long horizons when rewards are sparse. While a planner can provide subgoals to guide a low-level...
The paper introduces Reward Stimulation Implicit Q-Learning (RSIQL), a non-hierarchical approach to improve offline goal-conditioned reinforcement learning. RSIQL adds auxiliary reward signals at intermediate states that are predicted to aid progress toward the goal, thereby reducing the delay in training supervision. Experiments on D4RL goal-reaching benchmarks and OGBench demonstrate that RSIQL outperforms baseline goal-conditioned IQL and rivals hierarchical offline methods while maintaining a simple flat policy structure.
The paper identifies an informational curse of horizon in goal-conditioned policy learning, showing that longer goal relabeling horizons during training can hurt policy generalization and performance. Controlled experiments with oracle planners separate training horizons from test horizons, revealing that behavioral cloning policies degrade with longer horizons while reinforcement learning mitigates this effect. The authors attribute this to reduced conditional mutual information between actions and hindsight-relabeled goals, and demonstrate that distilling short-horizon policy Jacobians into long-horizon policies improves performance, especially in combinatorial manipulation tasks.
arXiv:2608. 08158v1 Announce Type: new Abstract: Sparse, delayed, and weakly informative rewards remain central obstacles to efficient reinforcement learning.
arXiv:2608. 05111v1 Announce Type: new Abstract: In partially observable reinforcement learning, agents face a dual bottleneck: they must explore to encounter rewarding states and retain that experience in memory to optimize their policies.
The paper introduces PRM4RL, a framework that combines task planning and dense reward generation using large language models for reinforcement learning. It decomposes complex tasks into manageable subtasks and employs a fine‑grained progress function to monitor execution, which is then used to construct a Progress Reward Model for potential‑based reward shaping. Experiments on robotics control tasks show that PRM4RL outperforms existing LLM‑based planning and reward methods, achieving state‑of‑the‑art performance.