no code implementations • 29 Nov 2023 • Amin Rakhsha, Mete Kemertas, Mohammad Ghavamzadeh, Amir-Massoud Farahmand
We propose and theoretically analyze an approach for planning with an approximate model in reinforcement learning that can reduce the adverse impact of model error.
no code implementations • 25 Nov 2022 • Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-Massoud Farahmand
We introduce new planning and reinforcement learning algorithms for discounted MDPs that utilize an approximate model of the environment to accelerate the convergence of the value function.
no code implementations • 16 Feb 2021 • Amin Rakhsha, Xuezhou Zhang, Xiaojin Zhu, Adish Singla
We study black-box reward poisoning attacks against reinforcement learning (RL), in which an adversary aims to manipulate the rewards to mislead a sequence of RL agents with unknown algorithms to learn a nefarious policy in an environment unknown to the adversary a priori.
no code implementations • 21 Nov 2020 • Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla
We provide lower/upper bounds on the attack cost, and instantiate our attacks in two settings: (i) an offline setting where the agent is doing planning in the poisoned environment, and (ii) an online setting where the agent is learning a policy with poisoned feedback.
1 code implementation • ICML 2020 • Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla
We study a security threat to reinforcement learning where an attacker poisons the learning environment to force the agent into executing a target policy chosen by the attacker.