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Conceptual Belief-Informed Reinforcement Learning

Xingrui Gu, Chuyi Jiang, Laixi Shi

TL;DR

The paper addresses RL sample inefficiency by drawing inspiration from cognitive science, proposing Conceptual Belief-Informed RL (HI-RL) which combines conceptual abstraction with adaptive concept-based priors. HI-RL is algorithm-agnostic and introduces two modules—Concept Formation and Conceptual Adaptive Belief—to reformulate past experiences as priors (b_t) and fuse them with current signals via a Smoothed Belief backup (B_t). It provides concrete instantiations (HI-Q, HI-PPO, HI-SAC) and demonstrates consistent gains in sample efficiency and performance across discrete and continuous domains, backed by a formalized problem setup, algorithmic details, and extensive experiments. By enabling belief-guided, concept-aware learning, HI-RL advances toward more human-like, efficient reinforcement learning and suggests a practical path to an Era of Experience.

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 policy updates. We evaluate HI-RL by integrating it into various existing value- and policy-based algorithms (DQN, PPO, SAC, and TD3) and demonstrate consistent improvements in sample efficiency and performance across both discrete and continuous control benchmarks.

Conceptual Belief-Informed Reinforcement Learning

TL;DR

The paper addresses RL sample inefficiency by drawing inspiration from cognitive science, proposing Conceptual Belief-Informed RL (HI-RL) which combines conceptual abstraction with adaptive concept-based priors. HI-RL is algorithm-agnostic and introduces two modules—Concept Formation and Conceptual Adaptive Belief—to reformulate past experiences as priors (b_t) and fuse them with current signals via a Smoothed Belief backup (B_t). It provides concrete instantiations (HI-Q, HI-PPO, HI-SAC) and demonstrates consistent gains in sample efficiency and performance across discrete and continuous domains, backed by a formalized problem setup, algorithmic details, and extensive experiments. By enabling belief-guided, concept-aware learning, HI-RL advances toward more human-like, efficient reinforcement learning and suggests a practical path to an Era of Experience.

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 policy updates. We evaluate HI-RL by integrating it into various existing value- and policy-based algorithms (DQN, PPO, SAC, and TD3) and demonstrate consistent improvements in sample efficiency and performance across both discrete and continuous control benchmarks.
Paper Structure (40 sections, 3 theorems, 65 equations, 6 figures, 4 tables, 5 algorithms)

This paper contains 40 sections, 3 theorems, 65 equations, 6 figures, 4 tables, 5 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Cognitive Science for Conceptual Learning
  4. Experience-Informed Reinforcement Learning
  5. Abstraction in Reinforcement Learning
  6. Problem Formulation
  7. Conceptual Belief-Informed Reinforcement Learning
  8. Concept Formation
  9. Conceptual Adaptive Belief for RL
  10. Algorithm Implementation
  11. Conceptual Belief-Informed Q-learning (HI-Q)
  12. Conceptual Belief-Informed Proximal Policy Optimization (HI-PPO)
  13. Conceptual Belief-Informed Soft Actor-Critic (HI-SAC)
  14. Experiment
  15. Comparative Performance of HI-RL and Baselines
  16. ...and 25 more sections

Key Result

Lemma A.1

Consider an MDP with state $s_{t+1}$ and actions $a$, along with Q-value estimates $Q_t(s_{t+1}, a)$. Let $q_t(a \mid s_{t+1})$ denote the probability of selecting action $a$ in state $s_{t+1}$. By Jensen's inequality:

Figures (6)

  • Figure 1: Standard RL (left) replays raw transitions, while HI-RL (right) organizes them into conceptual categories with adaptive beliefs, enabling abstraction and belief-guided learning.
  • Figure 2: Learning curves comparing HI-PPO and PPO (Atari tasks) as well as HI-TD3 and TD3 (Mujoco and Box2D tasks). HI-RL variants demonstrate faster convergence, higher sample efficiency, and reduced variance across diverse environments.
  • Figure 3: Cartpole, Acrobot, CarRacing, Lunar Lander and Bipedal Walker .
  • Figure 4: Various block types used in the MetaDrive environment. These blocks represent common road structures such as straight roads, ramps, forks, roundabouts, curves, T-intersections, and intersections, used for evaluating the vehicle's path planning and decision-making capabilities.
  • Figure 5: Ant, Humanoid, Reacher and Half Cheetah.
  • ...and 1 more figures

Theorems & Definitions (5)

  • Definition 4.1: Concept Formation in State Space
  • Lemma A.1: Jensen's Inequality for Q-values
  • Lemma A.2: Convergence of Smoothed Bellman Operator
  • Theorem A.3
  • proof