Q-learning is arguably one of the most applied representative reinforcement learning
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …

Robots such as drones, ground rovers, underwater vehicles and industrial robots have
increased in popularity in recent years. Many sectors have benefited from this by increasing …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Autonomous vehicles must be comprehensively evaluated before deployed in cities and
highways. However, most existing evaluation approaches for autonomous vehicles are static …

Importance Sampling (IS) is a widely used building block for a large variety of off-policy
estimation and learning algorithms. However, empirical and theoretical studies have …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Experience replay is a technique that allows off-policy reinforcement-learning methods to
reuse past experiences. The stability and speed of convergence of reinforcement learning …

Framed in positive terms, this report examines how technical AI research might be steered in
a manner that is more attentive to humanity's long-term prospects for survival as a species …

In reinforcement learning, importance sampling is a widely used method for evaluating an
expectation under the distribution of data of one policy when the data has in fact been …

We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG)
and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected …