6 citations · 6 across the 4 of their papers we have counts for
4 papers
IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse
Siyuan Li, Hao Li, Jin Zhang +3
Humans have the ability to reuse previously learned policies to solve new tasks quickly, and reinforcement learning (RL) agents can do the same by transferring knowledge from sourc…
Learning to Solve Tasks with Exploring Prior Behaviours
Ruiqi Zhu, Siyuan Li, Tianhong Dai +2
Demonstrations are widely used in Deep Reinforcement Learning (DRL) for facilitating solving tasks with sparse rewards. However, the tasks in real-world scenarios can often have va…
Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement Learning
Shijie Han, Siyuan Li, Bo An +2
Multi-agent reinforcement learning (MARL) is a prevalent learning paradigm for solving stochastic games. In most MARL studies, agents in a game are defined as teammates or enemies…
A Shared Representation for Photorealistic Driving Simulators
Saeed Saadatnejad, Siyuan Li, Taylor Mordan +1
A powerful simulator highly decreases the need for real-world tests when training and evaluating autonomous vehicles. Data-driven simulators flourished with the recent advancement…