The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian noise fail to
explore efficiently in some reinforcement learning tasks and yet, they perform well in many …

Exploring rich environments and evaluating one's actions without prior knowledge is
immensely challenging. In this paper, we propose Motif, a general method to interface such …

Existing approaches for improving generalization in deep reinforcement learning (RL) have
mostly focused on representation learning, neglecting RL-specific aspects such as …

This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement
learning and sequential decision making, covering value-based RL, policy-gradient …

Reasoning at multiple levels of temporal abstraction is one of the key attributes of
intelligence. In reinforcement learning, this is often modeled through temporally extended …

Selecting exploratory actions that generate a rich stream of experience for better learning is
a fundamental challenge in reinforcement learning (RL). An approach to tackle this problem …

The use of unmanned aerial vehicles (UAVS) has been suggested as a potential
communications alternative due to their fast implantation, which makes this resource an …

We identify and study the phenomenon of policy churn, that is, the rapid change of the
greedy policy in value-based reinforcement learning. Policy churn operates at a surprisingly …

In standard reinforcement learning setups, the agent receives observations and performs
actions at evenly spaced intervals. However, in many real-world settings, observations are …