9 citations · 11 across the 9 of their papers we have counts for
6 papers · 1 filter
Anytime-Constrained Equilibria in Polynomial Time
Jeremy McMahan
We extend anytime constraints to the Markov game setting and the corresponding solution concept of an anytime-constrained equilibrium (ACE). Then, we present a comprehensive theory…
Inception: Efficiently Computable Misinformation Attacks on Markov Games
Jeremy McMahan, Young Wu, Yudong Chen +2
We study security threats to Markov games due to information asymmetry and misinformation. We consider an attacker player who can spread misinformation about its reward function to…
Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time
Jeremy McMahan
We present a novel algorithm that efficiently computes near-optimal deterministic policies for constrained reinforcement learning (CRL) problems. Our approach combines three key id…
Optimal Attack and Defense for Reinforcement Learning
Jeremy McMahan, Young Wu, Xiaojin Zhu +1
To ensure the usefulness of Reinforcement Learning (RL) in real systems, it is crucial to ensure they are robust to noise and adversarial attacks. In adversarial RL, an external at…
Anytime-Constrained Reinforcement Learning
Jeremy McMahan, Xiaojin Zhu
We introduce and study constrained Markov Decision Processes (cMDPs) with anytime constraints. An anytime constraint requires the agent to never violate its budget at any point in…
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning
Young Wu, Jeremy McMahan, Xiaojin Zhu +1
In offline multi-agent reinforcement learning (MARL), agents estimate policies from a given dataset. We study reward-poisoning attacks in this setting where an exogenous attacker m…