Christopher Amato

On Improving Decentralized Hysteretic Deep Reinforcement Learning.

2018 · arXiv (Cornell University)

Recent successes of value-based multi-agent deep reinforcement learning employ optimism in value function by carefully controlling learning rate(Omidshafiei et al., 2017) or reducing update prob-ability (Palmer et al., 2018). We introduce a de-centralized quantile estimator: …

To Ask or Not to Ask: A User Annoyance Aware Preference Elicitation Framework for Social Robots

2020

In this paper we investigate how social robots can efficiently gather user preferences without exceeding the allowed user annoyance threshold. To do so, we use a Gazebo based simulated office environment with a TIAGo Steel …

Multi-agent reinforcement learning with directed exploration and selective memory reuse

2021

Many tasks require cooperation and coordination of multiple agents. Multi-agent reinforcement learning (MARL) can effectively learn solutions to these problems, but exploration and local optima problems are still open research topics. In this paper, we …

Unbiased Asymmetric Actor-Critic for Partially Observable Reinforcement Learning.

2021 · arXiv (Cornell University)

In partially observable reinforcement learning, offline training gives access to latent information which is not available during online training and/or execution, such as the system state. Asymmetric actor-critic methods exploit such information by training a …