Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration

by   Zijian Gao, et al.

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 including DeepMind Control Suite and Atari Suite, large-scale empirical experiments are conducted and the results demonstrate that DyMeCu outperforms competitive curiosity-based methods with or without extrinsic rewards. We will release the code to enhance reproducibility.


page 2

page 4

page 6

page 7


Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning

We present AIRS: Automatic Intrinsic Reward Shaping that intelligently a...

Go Beyond Imagination: Maximizing Episodic Reachability with World Models

Efficient exploration is a challenging topic in reinforcement learning, ...

Large-Scale Study of Curiosity-Driven Learning

Reinforcement learning algorithms rely on carefully engineering environm...

Rényi State Entropy for Exploration Acceleration in Reinforcement Learning

One of the most critical challenges in deep reinforcement learning is to...

Self-Supervised Exploration via Temporal Inconsistency in Reinforcement Learning

In real-world scenarios, reinforcement learning under sparse-reward syne...

Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study

Learning about many things can provide numerous benefits to a reinforcem...

Please sign up or login with your details

Forgot password? Click here to reset