Given time series data, how can we answer questions like what will happen in the
future?''and how did we get here?''These sorts of probabilistic inference questions are …

This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement
learning and sequential decision making, covering value-based RL, policy-gradient …

Representations are at the core of all deep reinforcement learning (RL) methods for both
Markov decision processes (MDPs) and partially observable Markov decision processes …

In this paper we investigate the properties of representations learned by deep reinforcement
learning systems. Much of the early work on representations for reinforcement learning …

The ability to predict upcoming events has been hypothesized to comprise a key aspect of
natural and machine cognition. This is supported by trends in deep reinforcement learning …

Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient,
adaptive, and explainable by learning an explicit model of the environment. While the …

Abstract Representation learning and exploration are among the key challenges for any
deep reinforcement learning agent. In this work, we provide a singular value decomposition …

It is vital to learn effective policies that can be transferred to different domains with dynamics
discrepancies in reinforcement learning (RL). In this paper, we consider dynamics …

Reinforcement Learning (RL) algorithms typically utilize learning and/or planning
techniques to derive effective policies. The integration of both approaches has proven to be …

Consider the problem of exploration in sparse-reward or reward-free environments, such as
in Montezuma's Revenge. In the curiosity-driven paradigm, the agent is rewarded for how …