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cs.LG2024
Analyzing and Bridging the Gap between Maximizing Total Reward and Discounted Reward in Deep Reinforcement Learning
Shuyu Yin, Fei Wen, Peilin Liu +1
The optimal objective is a fundamental aspect of reinforcement learning (RL), as it determines how policies are evaluated and optimized. While total return maximization is the idea…
cs.LG2024
Probing Implicit Bias in Semi-gradient Q-learning: Visualizing the Effective Loss Landscapes via the Fokker--Planck Equation
Shuyu Yin, Fei Wen, Peilin Liu +1
Semi-gradient Q-learning is applied in many fields, but due to the absence of an explicit loss function, studying its dynamics and implicit bias in the parameter space is challengi…
cs.LG2024★ 2 cited
Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling
Sili Huang, Jifeng Hu, Zhejian Yang +5
Recent works have shown the remarkable superiority of transformer models in reinforcement learning (RL), where the decision-making problem is formulated as sequential generation. T…