Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …

Constructing agents with planning capabilities has long been one of the main challenges in
the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge …

We propose an online training procedure for a transformer-based automated theorem
prover. Our approach leverages a new search algorithm, HyperTree Proof Search (HTPS) …

Reinforcement learning promises to solve complex sequential-decision problems
autonomously by specifying a high-level reward function only. However, reinforcement …

In this paper, we study the problem of learning vision-based dynamic manipulation skills
using a scalable reinforcement learning approach. We study this problem in the context of …

In this work we aim to solve a large collection of tasks using a single reinforcement learning
agent with a single set of parameters. A key challenge is to handle the increased amount of …

This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL),
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …

Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence
(AI) and represents a step toward building autonomous systems with a higher-level …

The deep reinforcement learning community has made several independent improvements
to the DQN algorithm. However, it is unclear which of these extensions are complementary …