In neural combinatorial optimization (CO), reinforcement learning (RL) can turn a deep
neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a …

Reinforcement learning is a branch of machine learning concerned with using experience
gained through interacting with the world and evaluative feedback to improve a system's …

We introduce a new count-based optimistic exploration algorithm for Reinforcement
Learning (RL) that is feasible in environments with high-dimensional state-action spaces …

Reinforcement learning offers to robotics a framework and set of tools for the design of
sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic …

We present a behaviour-based reinforcement learning approach, inspired by Brook's
subsumption architecture, in which simple fully connected networks are trained as reactive …

While off-policy reinforcement learning (RL) algorithms are sample efficient due to gradient-
based updates and data reuse in the replay buffer, they struggle with convergence to local …

In reinforcement learning, it is common to let an agent interact for a fixed amount of time with
its environment before resetting it and repeating the process in a series of episodes. The …

Evolution strategies have been demonstrated to have the strong ability to roughly train deep
neural networks and well accomplish reinforcement learning tasks. However, existing …

Reinforcement learning is becoming increasingly popular in machine learning communities
in academia and industry alike. Experimental successes in the past few years have hinted at …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …