Meta-Cognitive Reinforcement Learning with Self-Doubt and Recovery

2026-01-28

AI Summary

This work proposes a metacognitive reinforcement learning framework to address the challenge that existing methods struggle to assess their own learning reliability, often becoming overly conservative under noise or suffering catastrophic failure due to accumulated uncertainty. The framework introduces a meta-trust variable grounded in the stability of value prediction error (VPES), enabling agents to introspectively monitor, dynamically regulate, and recover trust in their learning process. By integrating fail-safe modulation with a progressive trust recovery mechanism, the approach significantly improves average returns and drastically reduces late-stage training failures in continuous control tasks with corrupted rewards. This enhances both the robustness and adaptability of agents operating in unreliable environments.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Robust reinforcement learning methods typically focus on suppressing unreliable experiences or corrupted rewards, but they lack the ability to reason about the reliability of their own learning process. As a result, such methods often either overreact to noise by becoming overly conservative or fail catastrophically when uncertainty accumulates. In this work, we propose a meta-cognitive reinforcement learning framework that enables an agent to assess, regulate, and recover its learning behavior based on internally estimated reliability signals. The proposed method introduces a meta-trust variable driven by Value Prediction Error Stability (VPES), which modulates learning dynamics via fail-safe regulation and gradual trust recovery. Experiments on continuous-control benchmarks with reward corruption demonstrate that recovery-enabled meta-cognitive control achieves higher average returns and significantly reduces late-stage training failures compared to strong robustness baselines.

Problem

• robust reinforcement learning
• learning reliability
• reward corruption
• uncertainty accumulation
• catastrophic failure

Innovation

• meta-cognitive reinforcement learning
• self-doubt
• trust recovery
• Value Prediction Error Stability
• robust RL