Conceptual Belief-Informed Reinforcement Learning
Xingrui Gu, Chuyi Jiang, Laixi Shi
TL;DR
This paper introduces Conceptual Belief-Informed Reinforcement Learning, which enhances sample efficiency and stability by integrating human-inspired concept abstraction and probabilistic beliefs into existing RL algorithms.
Contribution
It proposes a novel framework that forms high-level concepts and adaptive beliefs to improve learning efficiency, compatible with current RL methods.
Findings
Consistent improvements in sample efficiency across benchmarks.
Enhanced performance in both discrete and continuous control tasks.
Effective integration with multiple RL algorithms like DQN, PPO, SAC, and TD3.
Abstract
Reinforcement learning (RL) has achieved significant success but is hindered by inefficiency and instability, relying on large amounts of trial-and-error data and failing to efficiently use past experiences to guide decisions. However, humans achieve remarkably efficient learning from experience, attributed to abstracting concepts and updating associated probabilistic beliefs by integrating both uncertainty and prior knowledge, as observed by cognitive science. Inspired by this, we introduce Conceptual Belief-Informed Reinforcement Learning to emulate human intelligence (HI-RL), an efficient experience utilization paradigm that can be directly integrated into existing RL frameworks. HI-RL forms concepts by extracting high-level categories of critical environmental information and then constructs adaptive concept-associated probabilistic beliefs as experience priors to guide value or…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
- **Simple yet effective regularization:** The belief blending smooths updates, reduces variance, and improves sample efficiency. - **Experience reuse:** Clustering allows shared learning across similar states, enhancing generalization. - **Stability and transfer:** Beliefs act as priors, providing memory and consistency across episodes. - **Broad applicability:** Can be plugged into existing algorithms (DQN, PPO, SAC, TD3) without major architectural changes. - **Empirically strong:** Demonstra
- **Conceptually shallow:** Essentially applies K-means for context extraction and a Bayesian-style weighted update; not a fundamentally new algorithm. - **Lack of fair baselines:** Compared to plain SAC/PPO/TD3 but not to other context-aware approaches. - **Overuse of cognitive/“neuroscience” framing:** Adds narrative flair but little real mechanism. - **Concept formation quality:** If clustering fails to align with true behavioral modes, the priors can mislead learning. - **Hyperparameter sens
1. The proposed method is algorithm-agnostic. The authors demonstrate its ability to integrate with multiple RL algorithms (Q-learning, PPO, SAC) demonstrates versatility. 2. The mathematical formulation is generally correct and the integration mechanism is well-defined. I appreciate the derivation of convergence guarantee on the proposed smoothed Bellman Operator in the appendix. 3. Experimental evaluation spans multiple domains and algorithms, showing consistency. 4. I appreciate the good moti
1. As the main claim of the submission is to propose a new “experience utilization paradigm,” the critical comparison baselines should be comparing to other methods aimed at improving experience utilization, including replay methods (e.g. HER and its prioritized variants), episodic memory models (NEC), and state abstraction methods such as bi-simulation and contrastive learning (Patil et al, 2024). The current comparison baselines use only the base RL algorithms, which do not seem to be reasonab
I cannot judge since many important details missing or definitions are confusing
**Major:** - The idea of using "concepts" suggests that there exist previous environments agent has explored to form the concepts. This, on the other hand, means that the correct context of this work is domain adaptation or meta RL where past experiences are used to boost learning. - Results are reported only for the two baselines, DQN and SAC, that rather old methods and there have been many improvements for them. Even then, it is unclear in which cases the results are statistically signif
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Taxonomy
TopicsCognitive Science and Mapping · Q Methodology Applications
MethodsQ-Learning