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VPFlow argues that value prediction, not accurate state prediction, is what offline planning requires, training latent world models on closed-form Monte Carlo returns and planning in noise space via evolutionary search with a one-step flow policy. It surpasses model-free baselines by 30.3 and 10.1 percentage points on state and pixel OGBench benchmarks while outperforming latent planners like TD-MPC2.
论文提出 World-Model Policy Arbiter (WMPA)，一个测试时框架，通过在学习到的世界模型中推演多个冻结的目标条件策略并用共享价值函数评分，在 OGBench 上将宏平均成功率从 44% 提升至 58%。
The paper introduces Generalized Implicit Temporal Abstraction (GITA), a method for offline goal-conditioned reinforcement learning that trains a single k-conditioned value function across multiple temporal abstraction scales, resolving the trade-off between long-range value signal and local resolution. GITA outperforms offline GCRL baselines like HIQL and OTA on OGBench, raising average success rates by 25 percentage points over HIQL.
The paper proposes RSIQL, a non-hierarchical method for offline goal-conditioned reinforcement learning that uses selective reward stimulation to improve performance on benchmarks like D4RL.
PathBridger proposes a hierarchical offline goal-conditioned reinforcement learning method that explicitly connects subgoal selection with short-horizon execution through inverse dynamics modeling, achieving strong performance on benchmark tasks.
The paper introduces Safe Contrastive Reinforcement Learning (Safe-CRL) to address overestimation bias in goal-conditioned policy learning under failure termination, using one-bit failure signals to enhance safety and performance in robot navigation and locomotion tasks.
This paper proposes NFTR, a method for offline goal-conditioned reinforcement learning that uses normalizing flows for subgoal policies and a triangle-slack reweighting to address optimistic bias and mode collapse in hierarchical implicit Q-learning.