12 citations · 14 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 2 cited
Learning Diverse Policies with Soft Self-Generated Guidance
Guojian Wang, Faguo Wu, Xiao Zhang +1
Reinforcement learning (RL) with sparse and deceptive rewards is challenging because non-zero rewards are rarely obtained. Hence, the gradient calculated by the agent can be stocha…
cs.LG2024
Trajectory-Oriented Policy Optimization with Sparse Rewards
Guojian Wang, Faguo Wu, Xiao Zhang
Mastering deep reinforcement learning (DRL) proves challenging in tasks featuring scant rewards. These limited rewards merely signify whether the task is partially or entirely acco…
cs.LG2023★ 12 cited
Adaptive trajectory-constrained exploration strategy for deep reinforcement learning
Guojian Wang, Faguo Wu, Xiao Zhang +2
Deep reinforcement learning (DRL) faces significant challenges in addressing the hard-exploration problems in tasks with sparse or deceptive rewards and large state spaces. These c…