1 citations · 1 across the 9 of their papers we have counts for
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PRISM: Parallel Reward Integration with Symmetry for MORL
Finn van der Knaap, Kejiang Qian, Zheng Xu +1
This work studies heterogeneous Multi-Objective Reinforcement Learning (MORL), where objectives can differ sharply in temporal frequency. Such heterogeneity allows dense objectives…
StepScorer: Accelerating Reinforcement Learning with Step-wise Scoring and Psychological Regret Modeling
Zhe Xu
Reinforcement learning algorithms often suffer from slow convergence due to sparse reward signals, particularly in complex environments where feedback is delayed or infrequent. Thi…
Expediting Reinforcement Learning by Incorporating Knowledge About Temporal Causality in the Environment
Jan Corazza, Hadi Partovi Aria, Daniel Neider +1
Reinforcement learning (RL) algorithms struggle with learning optimal policies for tasks where reward feedback is sparse and depends on a complex sequence of events in the environm…
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information
Jan Corazza, Hadi Partovi Aria, Hyohun Kim +2
Reinforcement learning (RL) algorithms can find an optimal policy for a single agent to accomplish a particular task. However, many real-world problems require multiple agents to c…
DODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecasting
Eric Hanchen Jiang, Zhi Zhang, Dinghuai Zhang +9
Advancements in reinforcement learning have led to the development of sophisticated models capable of learning complex decision-making tasks. However, efficiently integrating world…