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cs.LG2025
Exploration by Random Reward Perturbation
Haozhe Ma, Guoji Fu, Zhengding Luo +2
We introduce Random Reward Perturbation (RRP), a novel exploration strategy for reinforcement learning (RL). Our theoretical analyses demonstrate that adding zero-mean noise to env…
cs.LG2024★ 1 cited
Centralized Reward Agent for Knowledge Sharing and Transfer in Multi-Task Reinforcement Learning
Haozhe Ma, Zhengding Luo, Thanh Vinh Vo +2
Reward shaping is effective in addressing the sparse-reward challenge in reinforcement learning (RL) by providing immediate feedback through auxiliary, informative rewards. Based o…
cs.LG2024★ 1 cited
Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning
Haozhe Ma, Zhengding Luo, Thanh Vinh Vo +2
Reward shaping is a technique in reinforcement learning that addresses the sparse-reward problem by providing more frequent and informative rewards. We introduce a self-adaptive an…