# ExTra: Transfer-guided Exploration

arXiv: https://arxiv.org/abs/1906.11785 · HTML: https://mlsanity.com/paper/1906.11785/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [MICo: Improved representations via sampling-based state similarity for Markov decision processes](https://mlsanity.com/paper/2106.08229/) (2021)
- [Understanding and Addressing the Pitfalls of Bisimulation-based Representations in Offline Reinforcement Learning](https://mlsanity.com/paper/2310.17139/) (2023)
- [A Generalized Bisimulation Metric of State Similarity between Markov Decision Processes: From Theoretical Propositions to Applications](https://mlsanity.com/paper/2509.18714/) (2025)

## Abstract (arXiv)

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.