mlsanityData to 7 Oct 2026

Meta-Cognitive Reinforcement Learning with Self-Doubt and Recovery

arXiv 2601.20193 ↗First posted 28 Jan 2026cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 41 references that are in the map. First posted on arXiv 28 Jan 2026.

Builds on

29 of 41 references.

References · 29

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  5. 2015Trust Region Policy Optimizationcited by 2,503
  6. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  7. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  8. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  9. 2017Constrained Policy Optimizationcited by 928
  10. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 813
  11. 2017Deep Reinforcement Learning that Matterscited by 727
  12. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
17 more
  1. 2016Deep Exploration via Bootstrapped DQNcited by 518
  2. 2017Robust Adversarial Reinforcement Learningcited by 439
  3. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
  4. 2019Self-Supervised Exploration via Disagreementcited by 263
  5. 2018Safe Exploration in Continuous Action Spacescited by 237
  6. 2020Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observationscited by 235
  7. 2018Machine Theory of Mindcited by 222
  8. 2020Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPOcited by 184
  9. 2020Revisiting Fundamentals of Experience Replaycited by 171
  10. 2018Meta-Gradient Reinforcement Learningcited by 156
  11. 2017Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environmentscited by 106
  12. 2019Robust Reinforcement Learning for Continuous Control with Model Misspecificationcited by 76
  13. 2020DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correctioncited by 71
  14. 2019Meta-Curvaturecited by 70
  15. 2017Reinforcement Learning with a Corrupted Reward Channelcited by 68
  16. 2019Learning an Adaptive Learning Rate Schedulecited by 29
  17. 2020A Self-Tuning Actor-Critic Algorithmcited by 6

Built on by

Not cited yet.