AdaGamma: State-Dependent Discounting for Temporal Adaptation in Reinforcement Learning

• URL: http://arxiv.org/abs/2605.06149v1
• Date: Thu, 07 May 2026 12:42:46 GMT
• Title: AdaGamma: State-Dependent Discounting for Temporal Adaptation in Reinforcement Learning
• Abstract summary: State-dependent discounting is conceptually appealing, but naive deep actor--critic implementations can degenerate toward TD-error collapse.<n>We propose AdaGamma, a practical deep actor--critic method for state-dependent discounting that learns a state-dependent discount function together with a return-consistency objective.
• Score: 13.721410595245894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The discount factor in reinforcement learning controls both the effective planning horizon and the strength of bootstrapping, yet most deep RL methods use a single fixed value across all states. While state-dependent discounting is conceptually appealing, naive deep actor--critic implementations can become unstable and degenerate toward TD-error collapse. We propose AdaGamma, a practical deep actor--critic method for state-dependent discounting that learns a state-dependent discount function together with a return-consistency objective to regularize the induced backup structure. On the theory side, we analyze the Bellman operator induced by state-dependent discounting and establish its basic well-posedness properties under suitable conditions. Empirically, AdaGamma integrates into both SAC and PPO, yielding consistent improvements on continuous-control benchmarks, and achieves statistically significant gains in an online A/B test on the JD Logistics platform. These results suggest that state-dependent discounting can be made effective in deep RL when coupled with a return-consistency objective that prevents degenerate target manipulation.

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