Conceptual Belief-Informed Reinforcement Learning

Conference: ICML 2025
arXiv: 2410.01739
Code: None
Area: Reinforcement Learning
Keywords: Sample Efficiency, Conceptual Abstraction, Bayesian Prior, Human Cognition-Inspired, Experience Utilization

TL;DR

Proposes HI-RL (Human Intelligence-RL), which integrates conceptual abstraction and probabilistic prior belief mechanisms from cognitive science into RL. It extracts high-level concepts from experience and constructs concept-associated adaptive priors to guide value function/policy updates, consistently improving the sample efficiency of DQN/PPO/SAC/TD3 as an algorithm-agnostic plug-in.

Background & Motivation

Background

Background: RL is successful but its sample efficiency lags far behind human learning, relying on vast amounts of trial-and-error interactions.

Limitations of Prior Work: Experience replay only operates at the "buffer level" (resampling/re-labeling) without extracting higher-level conceptual abstractions; Bayesian methods focus on uncertainty but are rarely combined with conceptual abstraction.

Key Challenge: Humans achieve highly efficient learning through "conceptualization" (abstracting experience into concepts and updating probabilistic beliefs), whereas RL lacks similar mechanisms.

Goal: Efficiently utilize past experience to accelerate RL learning.

Key Insight: Two mechanisms from cognitive science: (a) conceptual abstraction (extracting high-level categories from a large state space); (b) probabilistic priors (aggregating experience into adaptive priors to guide decision-making).

Core Idea: Extract concepts from the state space \(\rightarrow\) maintain probabilistic beliefs for each concept \(\rightarrow\) inject prior knowledge as auxiliary signals into value function/policy updates.

Method

Overall Architecture

1. Concept Extraction: Cluster experiences to obtain high-level state concepts.
2. Belief Construction: Maintain probabilistic priors of rewards/transitions for each concept.
3. Belief Injection: Incorporate prior information as auxiliary signals into existing RL algorithms.

Key Designs

1. Conceptual Abstraction Module:

   • Function: Organize experience from a large state space into a finite number of conceptual categories.
   • Mechanism: Cluster states in the experience replay (e.g., via K-Means), where each cluster represents a concept.
   • Design Motivation: Reduce the dimension of the belief space to achieve scalable prior estimation.

2. Probabilistic Belief Construction and Update:

   • Function: Maintain adaptive probabilistic priors for each concept.
   • Mechanism: Maintain a Bayesian posterior over rewards/transitions under concept \(c\), adapting dynamically with experience.
   • Design Motivation: Prior signals become increasingly accurate, accelerating the convergence of value estimation.

3. Algorithm-Agnostic Injection:

   • Function: Inject concept priors as auxiliary terms into any RL algorithm.
   • Mechanism: Add a prior guidance term during value function updates (DQN); add prior regularization during policy updates (PPO/SAC/TD3).
   • Design Motivation: Prevent altering the core logic of the original algorithm, serving as a purely incremental improvement.

Loss & Training

• Original algorithm loss + conceptual prior auxiliary loss.
• Applicable to both discrete (DQN) and continuous (PPO/SAC/TD3) settings.

Key Experimental Results

Main Results

Algorithm	Baseline Return	+HI-RL Return	Gain
DQN (CartPole)	195	200	+2.6% (Faster Convergence)
PPO (Hopper)	2100	2650	+26%
SAC (Ant)	3200	3800	+19%
TD3 (HalfCheetah)	8500	9200	+8%

Ablation Study

Configuration	Effect	Description
No Concepts (Global Prior)	Moderate improvement	Conceptual differentiation provides finer-grained priors
No Priors (Concepts Only)	Minor improvement	Concepts alone are insufficient; prior injection is required
Full HI-RL	Optimal	The two are complementary

Key Findings

• Consistent improvements are achieved across all 4 algorithms in both discrete and continuous environments.
• Optimal performance is achieved with 5-20 concepts; too few or too many concepts degrade performance.
• Plug-and-play capability—incorporating HI-RL typically introduces <5% computational overhead.

Highlights & Insights

• Cognitive-science-inspired RL framework—the combination of "concepts + beliefs" is natural and highly effective.
• Algorithm-agnostic design greatly enhances practicality.
• Orthogonal to experience replay—the two can be stack-utilized.

Limitations & Future Work

• Concept clustering is static (not updated during training); dynamic concept formulation might be superior.
• The number of clusters is a hyperparameter.
• Evaluation was only conducted on classical control tasks; complex visual tasks remain to be tested.

Rating

• Novelty: ⭐⭐⭐⭐ A valuable integration of cognitive science and RL
• Experimental Thoroughness: ⭐⭐⭐⭐ Evaluated on 4 algorithms across discrete and continuous environments
• Writing Quality: ⭐⭐⭐⭐ Clear motivation
• Value: ⭐⭐⭐⭐ A simple yet effective enhancement for RL

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