Transfer Reinforcement Learning under Unobserved Contextual Information

TL;DR

This paper introduces a method for transfer reinforcement learning that accounts for unobserved contextual information, using causal bounds to improve policy learning and reduce bias in data transfer scenarios.

Contribution

The paper develops a causal bounds approach for transfer RL under unobserved context, enabling unbiased value function estimation and improved learning efficiency.

Findings

Causal bounds effectively reduce bias in transfer RL.

Proposed algorithms accelerate learning with demonstrator data.

Validated on robot motion planning tasks.

Abstract

In this paper, we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context. Specifically, we consider a demonstrator agent that has access to a context-aware policy and can generate transition and reward data based on that policy. These data constitute the experience of the demonstrator. Then, the goal is to transfer this experience, excluding the underlying contextual information, to a learner agent that does not have access to the environmental context, so that they can learn a control policy using fewer samples. It is well known that, disregarding the causal effect of the contextual information, can introduce bias in the transition and reward models estimated by the learner, resulting in a learned suboptimal policy. To address this challenge, in this paper, we develop a method to obtain causal bounds on the transition and reward functions using the demonstrator's data, which we then use to obtain causal bounds on the value functions. Using these value function bounds, we propose new Q learning and UCB-Q learning algorithms that converge to the true value function without bias. We provide numerical experiments for robot motion planning problems that validate the proposed value function bounds and demonstrate that the proposed algorithms can effectively make use of the data from the demonstrator to accelerate the learning process of the learner.