# Transfer Reinforcement Learning under Unobserved Contextual Information

arXiv: https://arxiv.org/abs/2003.04427 · HTML: https://mlsanity.com/paper/2003.04427/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

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

- [Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning](https://mlsanity.com/paper/2106.03833/) (2021)
- [Deep Reinforcement Learning with Explicit Context Representation](https://mlsanity.com/paper/2310.09924/) (2023)
- [Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health](https://mlsanity.com/paper/2209.04356/) (2022)

## Abstract (arXiv)

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.