Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds
significant promise for capturing expert motor skills through efficient imitation, facilitating …

Recent research has revealed that the output of deep neural networks (DNNs) can be easily
altered by adding relatively small perturbations to the input vector. In this paper, we analyze …

We introduce a new algorithm for reinforcement learning called Maximum aposteriori Policy
Optimisation (MPO) based on coordinate ascent on a relative entropy objective. We show …

Some of the most successful applications of deep reinforcement learning to challenging
domains in discrete and continuous control have used policy gradient methods in the on …

Evolution strategies are a class of evolutionary algorithms for black-box optimization and
achieve state-of-the-art performance on many benchmarks and real-world applications …

In recent studies on model-based reinforcement learning (MBRL), incorporating uncertainty
in forward dynamics is a state-of-the-art strategy to enhance learning performance, making …

Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning
(RL) approach. However, we observe that in a continuous action space, PPO can …

We present an off-policy actor-critic algorithm for Reinforcement Learning (RL) that
combines ideas from gradient-free optimization via stochastic search with learned action …

With the increasing pace of automation, modern robotic systems need to act in stochastic,
non-stationary, partially observable environments. A range of algorithms for finding …

Robots that can learn in the physical world will be important to enable robots to escape their
stiff and pre-programmed movements. For dynamic high-acceleration tasks, such as …