14 citations · 14 across the 2 of their papers we have counts for
6 papers
Environment Shaping in Reinforcement Learning using State Abstraction
Parameswaran Kamalaruban, Rati Devidze, Volkan Cevher +1
One of the central challenges faced by a reinforcement learning (RL) agent is to effectively learn a (near-)optimal policy in environments with large state spaces having sparse and…
Understanding the Power and Limitations of Teaching with Imperfect Knowledge
Rati Devidze, Farnam Mansouri, Luis Haug +2
Machine teaching studies the interaction between a teacher and a student/learner where the teacher selects training examples for the learner to learn a specific task. The typical a…
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
Amin Rakhsha, Goran Radanovic, Rati Devidze +2
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. As…
Interactive Teaching Algorithms for Inverse Reinforcement Learning
Parameswaran Kamalaruban, Rati Devidze, Volkan Cevher +1
We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher. More formally, we tackle the following algorith…
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
Sebastian Tschiatschek, Ahana Ghosh, Luis Haug +2
Inverse reinforcement learning (IRL) enables an agent to learn complex behavior by observing demonstrations from a (near-)optimal policy. The typical assumption is that the learner…
Learning to Collaborate in Markov Decision Processes
Goran Radanovic, Rati Devidze, David C. Parkes +1
We consider a two-agent MDP framework where agents repeatedly solve a task in a collaborative setting. We study the problem of designing a learning algorithm for the first agent (A…