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We attack the state-of-the-art Go-playing AI system KataGo by training adversarial policies that play against frozen KataGo victims. …

It’s challenging to design reward functions for complex, real-world tasks. Reward learning lets one instead infer reward …

In many real-world applications, the reward function is too complex to be manually specified. In such cases, reward functions must …

For many tasks, the reward function is inaccessible to introspection or too complex to be specified procedurally, and must instead be …

The objective of many real-world tasks is complex and difficult to procedurally specify. This makes it necessary to use reward or …

In many real-world tasks, it is not possible to procedurally specify an RL agent’s reward function. In such cases, a reward …

Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to …

Deep reinforcement learning achieves superhuman performance in a range of video game environments, but requires that a designer …

Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring multiple reward functions from expert demonstrations. Prior …

Reward design, the problem of selecting an appropriate reward function for an AI system, is both critically important, as it encodes …