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 …

The deep reinforcement learning (DRL) community has published remarkable results on
complex strategic planning problems, most famously in virtual scenarios for board and video …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

Autonomous agents optimize the reward function we give them. What they don't know is how
hard it is for us to design a reward function that actually captures what we want. When …

Reinforcement learning algorithms describe how an agent can learn an optimal action policy
in a sequential decision process, through repeated experience. In a given environment, the …

Policy gradient methods have become a standard for training reinforcement learning agents
in a scalable and efficient manner. However, they do not account for transition uncertainty …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the
returns. A risk measure often focuses on the worst returns out of the agent's experience. As a …

Convex Reinforcement Learning (RL) is a recently introduced framework that generalizes
the standard RL objective to any convex (or concave) function of the state distribution …

While maximizing expected return is the goal in most reinforcement learning approaches,
risk-sensitive objectives such as conditional value at risk (CVaR) are more suitable for many …