15 citations · 35 across the 5 of their papers we have counts for
5 papers
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning
Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
In this paper we explore how actor-critic methods in deep reinforcement learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be extended with agent modeling. Insp…
Interactive Learning of Environment Dynamics for Sequential Tasks
Robert Loftin, Bei Peng, Matthew E. Taylor +2
In order for robots and other artificial agents to efficiently learn to perform useful tasks defined by an end user, they must understand not only the goals of those tasks, but als…
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition
Chao Gao, Pablo Hernandez-Leal, Bilal Kartal +1
The Pommerman Team Environment is a recently proposed benchmark which involves a multi-agent domain with challenges such as partial observability, decentralized execution (without…
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman
Bilal Kartal, Pablo Hernandez-Leal, Chao Gao +1
Safe reinforcement learning has many variants and it is still an open research problem. Here, we focus on how to use action guidance by means of a non-expert demonstrator to avoid…
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning
Gabriel V. de la Cruz, Yunshu Du, Matthew E. Taylor
Deep Reinforcement Learning (DRL) algorithms are known to be data inefficient. One reason is that a DRL agent learns both the feature and the policy tabula rasa. Integrating prior…