ExTra: Transfer-guided Exploration

TL;DR

ExTra introduces a transfer-guided exploration method in reinforcement learning that leverages optimal policies from related tasks to improve convergence rates and robustness across different environments.

Contribution

The paper proposes a novel transfer-guided exploration method using bisimulation distances to guide action sampling, enhancing convergence and robustness in RL.

Findings

ExTra outperforms traditional exploration strategies in gridworlds.

ExTra is robust to source task dissimilarity.

Combining ExTra with traditional methods improves convergence.

Abstract

In this work we present a novel approach for transfer-guided exploration in reinforcement learning that is inspired by the human tendency to leverage experiences from similar encounters in the past while navigating a new task. Given an optimal policy in a related task-environment, we show that its bisimulation distance from the current task-environment gives a lower bound on the optimal advantage of state-action pairs in the current task-environment. Transfer-guided Exploration (ExTra) samples actions from a Softmax distribution over these lower bounds. In this way, actions with potentially higher optimum advantage are sampled more frequently. In our experiments on gridworld environments, we demonstrate that given access to an optimal policy in a related task-environment, ExTra can outperform popular domain-specific exploration strategies viz. epsilon greedy, Model-Based Interval Estimation - Exploration Bonus (MBIE-EB), Pursuit and Boltzmann in rate of convergence. We further show that ExTra is robust to choices of source task and shows a graceful degradation of performance as the dissimilarity of the source task increases. We also demonstrate that ExTra, when used alongside traditional exploration algorithms, improves their rate of convergence. Thus it is capable of complementing the efficacy of traditional exploration algorithms.