37 citations · 87 across the 10 of their papers we have counts for
11 papers
Reward Design for Reinforcement Learning Agents
Rati Devidze
Reward functions are central in reinforcement learning (RL), guiding agents towards optimal decision-making. The complexity of RL tasks requires meticulously designed reward functi…
Informativeness of Reward Functions in Reinforcement Learning
Rati Devidze, Parameswaran Kamalaruban, Adish Singla
Reward functions are central in specifying the task we want a reinforcement learning agent to perform. Given a task and desired optimal behavior, we study the problem of designing…
Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement Learning
Weichen Li, Rati Devidze, Sophie Fellenz
Text-based games are a popular testbed for language-based reinforcement learning (RL). In previous work, deep Q-learning is commonly used as the learning agent. Q-learning algorith…
Curriculum Design for Teaching via Demonstrations: Theory and Applications
Gaurav Yengera, Rati Devidze, Parameswaran Kamalaruban +1
We consider the problem of teaching via demonstrations in sequential decision-making settings. In particular, we study how to design a personalized curriculum over demonstrations t…
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks
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…
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…