Reinforcement learning (RL) algorithms update an agent's parameters according to one of
several possible rules, discovered manually through years of research. Automating the …

Reinforcement learning and games have a long and mutually beneficial common history.
From one side, games are rich and challenging domains for testing reinforcement learning …

Reinforcement learning (RL) is a set of mathematical methods and algorithms that can be
applied to a wide array of problems and plays a central role in machine learning. The aim of …

Reinforcement learning is a mathematical framework for developing computer agents that
can learn an optimal behavior by relating generic reward signals with its past actions. With …

Reinforcement learning methods have recently been very successful at performing complex
sequential tasks like playing Atari games, Go and Poker. These algorithms have …

We contribute Policy Reuse as a technique to improve a reinforcement learning agent with
guidance from past learned similar policies. Our method relies on using the past policies as …

The combination of reinforcement learning with deep learning is a promising approach to
tackle important sequential decision-making problems that are currently intractable. One …

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 …

Atari games have been a long-standing benchmark in the reinforcement learning (RL)
community for the past decade. This benchmark was proposed to test general competency …

We propose a method for meta-learning reinforcement learning algorithms by searching
over the space of computational graphs which compute the loss function for a value-based …