28 citations · 82 across the 6 of their papers we have counts for
6 papers
A Multi-modal and Multi-task Learning Method for Action Unit and Expression Recognition
Yue Jin, Tianqing Zheng, Chao Gao +1
Analyzing human affect is vital for human-computer interaction systems. Most methods are developed in restricted scenarios which are not practical for in-the-wild settings. The Aff…
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
Xiangjun Wang, Junxiao Song, Penghui Qi +9
AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy…
On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman
Chao Gao, Bilal Kartal, Pablo Hernandez-Leal +1
How to best explore in domains with sparse, delayed, and deceptive rewards is an important open problem for reinforcement learning (RL). This paper considers one such domain, the r…
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…
Continual Match Based Training in Pommerman: Technical Report
Peng Peng, Liang Pang, Yufeng Yuan +1
Continual learning is the ability of agents to improve their capacities throughout multiple tasks continually. While recent works in the literature of continual learning mostly foc…