Reinforcement learning for semi-autonomous approximate quantum eigensolver

Autonomous transfer for reinforcement learning

Recent work in transfer learning has succeeded in making reinforcement learning algorithms more efficient by incorporating knowledge from previous tasks. However, such methods typically must be provided either a full model of the tasks or an explicit relation mapping one task into the other. An autonomous agent may not have access to such high-level information, but would be able to analyze its...

Safe Autonomous Reinforcement Learning

In the thesis we propose, we focus on equipping existing Reinforcement Learning algorithms with different kinds of safety constraints imposed on the exploration scheme. Common Reinforcement Learning algorithms are (sometimes implicitly) assumed to work in an ergodic1, or even “restartable” environment. However, these conditions are not achievable in field robotics, where the expensive robots ca...

Dual Control for Approximate Bayesian Reinforcement Learning

Control of non-episodic, finite-horizon dynamical systems with uncertain dynamics poses a tough and elementary case of the exploration-exploitation trade-off. Bayesian reinforcement learning, reasoning about the effect of actions and future observations, offers a principled solution, but is intractable. We review, then extend an old approximate approach from control theory—where the problem is ...

Approximately Optimal Approximate Reinforcement Learning

Deep Reinforcement Learning framework for Autonomous Driving

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework fo...