New approach unifies reinforcement learning generalization across rewards and dynamics

Researchers have introduced robust successor features, a novel approach that unifies generalization in reinforcement learning across both reward functions and transition kernels. This method is particularly effective for linear Markov Decision Processes where the transition kernel is uncertain. The work provides a theoretical bound on Generalized Policy Improvement, quantifying performance degradation due to transition kernel mismatches and recovering existing successor-feature guarantees when dynamics are consistent. The effectiveness of these robust successor features has been demonstrated on grid-based benchmarks, outperforming prior methods that only addressed reward or transition generalization. AI

IMPACT Enhances generalization in reinforcement learning for uncertain environments, potentially improving agent performance in complex, real-world scenarios.

RANK_REASON This is a research paper published on arXiv detailing a new method in reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Generalized Policy Improvement
• Markov decision processes
• operations research
• reinforcement learning
• Robust RLS Wiener FIR Filter for Signal Estimation in Linear Discrete-Time Stochastic Systems with Uncertain Parameters
• robust successor features
• successor representation

• paper
• other

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