A common challenge in reinforcement learning is how to convert the agent's interactions
with an environment into fast and robust learning. For instance, earlier work makes use of …

Deep reinforcement learning has shown remarkable success in the past few years. Highly
complex sequential decision making problems have been solved in tasks such as game …

We present Park, a platform for researchers to experiment with Reinforcement Learning (RL)
for computer systems. Using RL for improving the performance of systems has a lot of …

Multi-agent deep reinforcement learning (MADRL) has been widely used in many scenarios
such as robotics and game AI. However, existing methods mainly focus on the optimization …

Recent successes in applying Deep Learning techniques on Reinforcement Learning
algorithms have led to a wave of breakthrough developments in agent theory and …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in
reinforcement learning (RL) research. However, RL systems, when applied to large-scale …

Abstract Deep Reinforcement Learning is a topic that has gained a lot of attention recently,
due to the unprecedented achievements and remarkable performance of such algorithms in …

Reinforcement learning systems require good representations to work well. For decades
practical success in reinforcement learning was limited to small domains. Deep …

Reinforcement learning algorithms rely on exploration to discover new behaviors, which is
typically achieved by following a stochastic policy. In continuous control tasks, policies with a …