Setting Up Experimental Bell Tests with Reinforcement Learning

Speeding-up Reinforcement Learning with

Speeding Up Reinforcement Learning with Behavior Transfer

Reinforcement learning (RL) methods (Sutton & Barto 1998) have become popular machine learning techniques in recent years. RL has had some experimental successes and has been shown to exhibit some desirable properties in theory, but it has often been found very slow in practice. In this paper we introduce behavior transfer, a novel approach to speeding up traditional RL. We present experimental...

Setting up a Reinforcement Learning Task with a Real-World Robot

Reinforcement learning is a promising approach to developing hard-to-engineer adaptive solutions for complex and diverse robotic tasks. However, learning with real-world robots is often unreliable and difficult, which resulted in their low adoption in reinforcement learning research. This difficulty is worsened by the lack of guidelines for setting up learning tasks with robots. In this work, w...

Tests of Bell inequalities

According to recent reports, the last loopholes in testing Bell’s inequality are closed. It is argued that the really important task in this field has not been tackled yet and that the leading experiments claiming to close locality and detection efficiency loopholes, although making very significant progress, have conceptual drawbacks. The important task is constructing quantum devices which wi...

Scaling Up Reinforcement Learning through Targeted Exploration

Recent Reinforcement Learning (RL) algorithms, such as RMAX, make (with high probability) only a small number of poor decisions. In practice, these algorithms do not scale well as the number of states grows because the algorithms spend too much effort exploring. We introduce an RL algorithm State TArgeted R-MAX (STAR-MAX) that explores a subset of the state space, called the exploration envelop...