Many reinforcement learning (RL) tasks provide the agent with high-dimensional
observations that can be simplified into low-dimensional continuous states. To formalize this …

While auxiliary tasks play a key role in sha** the representations learnt by reinforcement
learning agents, much is still unknown about the mechanisms through which this is …

The first comprehensive guide to distributional reinforcement learning, providing a new
mathematical formalism for thinking about decisions from a probabilistic perspective …

We propose a new perspective on representation learning in reinforcement learning based
on geometric properties of the space of value functions. From there, we provide formal …

Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …

In reinforcement learning, state representations are used to tractably deal with large problem
spaces. State representations serve both to approximate the value function with few …

The recent emergence of reinforcement learning (RL) has created a demand for robust
statistical inference methods for the parameter estimates computed using these algorithms …

Reinforcement learning with function approximation can be unstable and even divergent,
especially when combined with off-policy learning and Bellman updates. In deep …

Abstract Offline Reinforcement Learning (RL) aims to address the challenge of distribution
shift between the dataset and the learned policy, where the value of out-of-distribution …

We study representation learning for Offline Reinforcement Learning (RL), focusing on the
important task of Offline Policy Evaluation (OPE). Recent work shows that, in contrast to …