The article discusses the use of deep generative models in offline reinforcement learning and the computational challenges posed by their large model size. To address this, the authors propose a…
Continue readingThe article introduces the Critic-Guided Decision Transformer (CGDT), a novel approach to offline reinforcement learning (RL). Traditional Return-Conditioned Supervised Learning (RCSL) struggles with stochastic environments and diverse future trajectory distributions….
Continue readingThe research focuses on the challenges of offline reinforcement learning (RL), particularly its vulnerability to data corruption. The authors investigate the performance of various offline RL algorithms under different types…
Continue readingThe paper discusses the challenges of offline reinforcement learning (RL) when dealing with imbalanced datasets that are dominated by suboptimal trajectories. It finds that current offline RL algorithms tend to…
Continue readingThe paper proposes an algorithm, Pessimistic Nonlinear Least-Square Value Iteration (PNLSVI), for offline reinforcement learning with non-linear function approximation. The algorithm includes three innovative components: a variance-based weighted regression scheme,…
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