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Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
[article]
2021
arXiv
pre-print
In real-world tasks, reinforcement learning (RL) agents frequently encounter situations that are not present during training time. To ensure reliable performance, the RL agents need to exhibit robustness against worst-case situations. The robust RL framework addresses this challenge via a worst-case optimization between an agent and an adversary. Previous robust RL algorithms are either sample inefficient, lack robustness guarantees, or do not scale to large problems. We propose the Robust
arXiv:2103.10369v1
fatcat:nqr7bcaugnfprolnvy5cq6c2iu