A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation

Marginalized importance sampling (MIS), which measures the density ratio between the state-action occupancy of a target policy and that of a sampling distribution, is a promising approach for off-policy evaluation. However, current state-of-the-art MIS methods rely on complex optimization tricks and succeed mostly on simple toy problems. We bridge the gap between MIS and deep reinforcement learning by observing that the density ratio can be computed from the successor representation of the target policy. The successor representation can be trained through deep reinforcement learning methodology and decouples the reward optimization from the dynamics of the environment, making the resulting algorithm stable and applicable to high-dimensional domains. We evaluate the empirical performance of our approach on a variety of challenging Atari and MuJoCo environments.Comment: ICML 202

An introduction to reinforcement learning for neuroscience

Reinforcement learning has a rich history in neuroscience, from early work on dopamine as a reward prediction error signal for temporal difference learning (Schultz et al., 1997) to recent work suggesting that dopamine could implement a form of 'distributional reinforcement learning' popularized in deep learning (Dabney et al., 2020). Throughout this literature, there has been a tight link between theoretical advances in reinforcement learning and neuroscientific experiments and findings. As a result, the theories describing our experimental data have become increasingly complex and difficult to navigate. In this review, we cover the basic theory underlying classical work in reinforcement learning and build up to an introductory overview of methods used in modern deep reinforcement learning that have found applications in systems neuroscience. We start with an overview of the reinforcement learning problem and classical temporal difference algorithms, followed by a discussion of 'model-free' and 'model-based' reinforcement learning together with methods such as DYNA and successor representations that fall in between these two categories. Throughout these sections, we highlight the close parallels between the machine learning methods and related work in both experimental and theoretical neuroscience. We then provide an introduction to deep reinforcement learning with examples of how these methods have been used to model different learning phenomena in the systems neuroscience literature, such as meta-reinforcement learning (Wang et al., 2018) and distributional reinforcement learning (Dabney et al., 2020). Code that implements the methods discussed in this work and generates the figures is also provided.Comment: Code available at: https://colab.research.google.com/drive/1kWOz2Uxn0cf2c4YizqIXQKWyxeYd6wvL?usp=sharin

Count-Based Exploration with the Successor Representation

In this paper we introduce a simple approach for exploration in reinforcement learning (RL) that allows us to develop theoretically justified algorithms in the tabular case but that is also extendable to settings where function approximation is required. Our approach is based on the successor representation (SR), which was originally introduced as a representation defining state generalization by the similarity of successor states. Here we show that the norm of the SR, while it is being learned, can be used as a reward bonus to incentivize exploration. In order to better understand this transient behavior of the norm of the SR we introduce the substochastic successor representation (SSR) and we show that it implicitly counts the number of times each state (or feature) has been observed. We use this result to introduce an algorithm that performs as well as some theoretically sample-efficient approaches. Finally, we extend these ideas to a deep RL algorithm and show that it achieves state-of-the-art performance in Atari 2600 games when in a low sample-complexity regime.Comment: This paper appears in the Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020