Reinforcement Learning
The MAGICAL Benchmark for Robust Imitation
The robot could learn from these demonstrations to complete the tasks autonomously. For IL algorithms to be useful, however, they must be able to learn how to perform tasks from few demonstrations. A domestic robot wouldn't be very helpful if it required thirty demonstrations before it figured out that you are deliberately washing your purple cravat
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting
We study reinforcement learning in non-episodic factored Markov decision processes (FMDPs).
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
Our analyses indicate that the explorability or reachability assumptions, previously made for the latter two settings, are not necessary statistically for reward-free exploration.
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
Our analyses indicate that the explorability or reachability assumptions, previously made for the latter two settings, are not necessary statistically for reward-free exploration.
Learning to Play No-Press Diplomacy with Best Response Policy Iteration Thomas Anthony
We consider Diplomacy, a 7-player board game designed to accentuate dilemmas resulting from many-agent interactions.
Provably Efficient Multi-Task Reinforcement Learning with Model Transfer
We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes (MDPs).
