Fast and Safe Policy Adaptation via Alignmentbased Transfer Applying deep reinforcement learning to physical systems, as opposed to learning in simulation, presents additional challenges in terms of sample efficiency and safety. Collecting large amounts of hardware demonstration data is timeconsuming and the exploratory behavior of reinforcement learning algorithms may lead the system into dangerous states, especially during the early stages of training. To address these challenges, we apply transfer learning to reuse a previously learned policy instead of learning from scratch. In this paper, we propose a method where given a source policy, policy adaptation is performed via transfer learning to produce a target policy suitable for realworld deployment. For policy adaptation, alignmentbased transfer learning is applied to trajectories generated by the source policy and their corresponding safe target trajectories. We apply this method to manipulators and show that the proposed method is applicable to both intertask and interrobot transfer whilst considering safety. We also show that the resulting target policy is robust and can be further improved with reinforcement learning.

On Training Flexible Robots using Deep Reinforcement Learning The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical models of their hardware have proven very effective. However, in many realworld settings, the uncertainties of the environment, the safety requirements and generalized capabilities that are expected of robots make rigid industrial robots unsuitable. This created great research interest in developing control strategies for flexible robot hardware for which building dynamical models are challenging. In this paper, inspired by the success of deep reinforcement learning (DRL), we systematically study the efficacy of policy search methods using DRL in training flexible robots. Our results indicate that DRL is successfully able to learn efficient and robust policies for complex tasks at various degrees of flexibility. We also note that DRL using Deep Deterministic Policy Gradients can be sensitive to the choice of sensors and adding more informative sensors does not necessarily make the task easier to learn.