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# System prepended metadata

title: gsac paper

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# gsac paper
A Dissection of Overfitting and Generalization in
Continuous Reinforcement Learning
https://arxiv.org/pdf/1806.07937.pdf

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
https://arxiv.org/abs/2006.14911
Q1. Can autonomous driving, imitation-learning, epistemic-uncertainty unaware methods detect distribution shifts? Q2. How robust are these methods under distribution shifts, i.e., can they recover? Q3. Does RIP’s (robust imitative planning) epistemic uncertainty quantification enable identification of novel scenes? Q4. Does RIP’s explicit mechanism for recovery from distribution shifts lead to improved performance?

A Survey of Generalisation in Deep Reinforcement Learning
https://arxiv.org/abs/2111.09794


Offline Reinforcement Learning: Tutorial, Review,
and Perspectives on Open Problems
https://arxiv.org/pdf/2005.01643.pdf

Robust Adversarial Reinforcement Learning
push agents
http://proceedings.mlr.press/v70/pinto17a/pinto17a.pdf