Dynamic Memory-based Curiosity: A Bootstrap Approach For Exploration

Abstract

The sparsity of extrinsic rewards poses a serious challenge for reinforcement learning (RL). Currently, many efforts have been made on curiosity which can provide a representative intrinsic reward for effective exploration. However, the challenge is still far from being solved. In this paper, we present a novel curiosity for RL, named DyMeCu, which stands for Dynamic Memory-based Curiosity. Inspired by human curiosity and information theory, DyMeCu consists of a dynamic memory and dual online learners. The curiosity arouses if memorized information can not deal with the current state, and the information gap between dual learners can be formulated as the intrinsic reward for agents, and then such state information can be consolidated into the dynamic memory. Compared with previous curiosity methods, DyMeCu can better mimic human curiosity with dynamic memory, and the memory module can be dynamically grown based on a bootstrap paradigm with dual learners. On multiple benchmarks includin

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keygao2022dynamic

Related papers

• Curiosity-driven Multi-agent Exploration With Mixed Objectives (2022)0.00
• Wonder Wins Ways: Curiosity-driven Exploration Through Multi-agent Contextual Calibration (2025)0.00
• Episodic Curiosity Through Reachability (2018)0.00
• Curiosity-driven Exploration In Sparse-reward Multi-agent Reinforcement Learning (2023)0.00
• Prototyping Three Key Properties Of Specific Curiosity In Computational Reinforcement Learning (2022)0.00
• Curiosity-driven Exploration Via Latent Bayesian Surprise (2021)0.00
• Is Curiosity All You Need? On The Utility Of Emergent Behaviours From Curious Exploration (2021)0.00
• Self-supervised Exploration Via Temporal Inconsistency In Reinforcement Learning (2022)3.58