Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration

TL;DR

This paper introduces a novel exploration method for reinforcement learning that maximizes value-conditional state entropy, effectively balancing exploration between high- and low-value states to accelerate learning across diverse benchmarks.

Contribution

It proposes a new exploration technique that maximizes value-conditional state entropy, addressing limitations of traditional state entropy methods in supervised RL settings.

Findings

Significantly accelerates RL algorithms on multiple benchmarks.

Effectively balances exploration between high- and low-value states.

Demonstrates robustness across MiniGrid, DeepMind Control Suite, and Meta-World.

Abstract

A promising technique for exploration is to maximize the entropy of visited state distribution, i.e., state entropy, by encouraging uniform coverage of visited state space. While it has been effective for an unsupervised setup, it tends to struggle in a supervised setup with a task reward, where an agent prefers to visit high-value states to exploit the task reward. Such a preference can cause an imbalance between the distributions of high-value states and low-value states, which biases exploration towards low-value state regions as a result of the state entropy increasing when the distribution becomes more uniform. This issue is exacerbated when high-value states are narrowly distributed within the state space, making it difficult for the agent to complete the tasks. In this paper, we present a novel exploration technique that maximizes the value-conditional state entropy, which separately estimates the state entropies that are conditioned on the value estimates of each state, then maximizes their average. By only considering the visited states with similar value estimates for computing the intrinsic bonus, our method prevents the distribution of low-value states from affecting exploration around high-value states, and vice versa. We demonstrate that the proposed alternative to the state entropy baseline significantly accelerates various reinforcement learning algorithms across a variety of tasks within MiniGrid, DeepMind Control Suite, and Meta-World benchmarks. Source code is available at https://sites.google.com/view/rl-vcse.