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StepScorer: Accelerating Reinforcement Learning with Step-wise Scoring and Psychological Regret Modeling

Zhe Xu

TL;DR

The paper tackles slow RL convergence under sparse rewards by introducing the Psychological Regret Model (PRM), a step-wise regret-based feedback mechanism. PRM uses a pre-trained opponent policy to estimate the regret between taken and optimal actions, converting sparse rewards into dense signals via reward shaping and augmentation. The method integrates with PPO and demonstrates a ~36% faster convergence and improved final performance on LunarLander-v3, with robustness in continuous control and delayed-feedback settings. The work bridges behavioral economics and RL, and points to practical implications for robotics, finance, and adaptive education, plus potential for continual learning with adapters.

Abstract

Reinforcement learning algorithms often suffer from slow convergence due to sparse reward signals, particularly in complex environments where feedback is delayed or infrequent. This paper introduces the Psychological Regret Model (PRM), a novel approach that accelerates learning by incorporating regret-based feedback signals after each decision step. Rather than waiting for terminal rewards, PRM computes a regret signal based on the difference between the expected value of the optimal action and the value of the action taken in each state. This transforms sparse rewards into dense feedback signals through a step-wise scoring framework, enabling faster convergence. We demonstrate that PRM achieves stable performance approximately 36\% faster than traditional Proximal Policy Optimization (PPO) in benchmark environments such as Lunar Lander. Our results indicate that PRM is particularly effective in continuous control tasks and environments with delayed feedback, making it suitable for real-world applications such as robotics, finance, and adaptive education where rapid policy adaptation is critical. The approach formalizes human-inspired counterfactual thinking as a computable regret signal, bridging behavioral economics and reinforcement learning.

StepScorer: Accelerating Reinforcement Learning with Step-wise Scoring and Psychological Regret Modeling

TL;DR

The paper tackles slow RL convergence under sparse rewards by introducing the Psychological Regret Model (PRM), a step-wise regret-based feedback mechanism. PRM uses a pre-trained opponent policy to estimate the regret between taken and optimal actions, converting sparse rewards into dense signals via reward shaping and augmentation. The method integrates with PPO and demonstrates a ~36% faster convergence and improved final performance on LunarLander-v3, with robustness in continuous control and delayed-feedback settings. The work bridges behavioral economics and RL, and points to practical implications for robotics, finance, and adaptive education, plus potential for continual learning with adapters.

Abstract

Reinforcement learning algorithms often suffer from slow convergence due to sparse reward signals, particularly in complex environments where feedback is delayed or infrequent. This paper introduces the Psychological Regret Model (PRM), a novel approach that accelerates learning by incorporating regret-based feedback signals after each decision step. Rather than waiting for terminal rewards, PRM computes a regret signal based on the difference between the expected value of the optimal action and the value of the action taken in each state. This transforms sparse rewards into dense feedback signals through a step-wise scoring framework, enabling faster convergence. We demonstrate that PRM achieves stable performance approximately 36\% faster than traditional Proximal Policy Optimization (PPO) in benchmark environments such as Lunar Lander. Our results indicate that PRM is particularly effective in continuous control tasks and environments with delayed feedback, making it suitable for real-world applications such as robotics, finance, and adaptive education where rapid policy adaptation is critical. The approach formalizes human-inspired counterfactual thinking as a computable regret signal, bridging behavioral economics and reinforcement learning.
Paper Structure (21 sections, 6 equations, 1 figure, 2 tables, 1 algorithm)

This paper contains 21 sections, 6 equations, 1 figure, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Problem Formulation
  5. Step-wise Scoring Framework
  6. Psychological Regret Model (PRM)
  7. Potential-Based Reward Shaping Implementation
  8. Integration with Policy Optimization
  9. Algorithm
  10. Experimental Setup
  11. Environments
  12. Baselines
  13. Implementation Details
  14. Evaluation Protocol
  15. Results
  16. ...and 6 more sections

Figures (1)

  • Figure 1: Learning curves comparing PPO baseline (blue) with PPO+PRM (orange) on Lunar Lander. PRM converges significantly faster, achieving the solved threshold in 350 episodes vs >550 for baseline. The teal dashed line represents the solved threshold (Reward = 200). Shaded regions represent standard deviation ranges across 5 random seeds.