Reinforcement learning (RL) algorithms update an agent's parameters according to one of
several possible rules, discovered manually through years of research. Automating the …

Deep reinforcement learning includes a broad family of algorithms that parameterise an
internal representation, such as a value function or policy, by a deep neural network. Each …

Designing reward functions for efficiently guiding reinforcement learning (RL) agents toward
specific behaviors is a complex task. This is challenging since it requires the identification of …

This paper presents a Double Deep Q-Network algorithm for trading single assets, namely
the E-mini S&P 500 continuous futures contract. We use a proven setup as the foundation for …

Discounting the influence of future events is a key paradigm in economics and it is widely
used in computer-science models, such as games, Markov decision processes (MDPs) …

Meta-gradient reinforcement learning (RL) algorithms have substantially boosted the
performance of RL agents by learning an adaptive return. All the existing algorithms adhere …

How much credit (or blame) should an action taken in a state get for a future reward? This is
the fundamental temporal credit assignment problem in Reinforcement Learning (RL). One …

We work towards a unifying paradigm for accelerating policy optimization methods in
reinforcement learning (RL) through\emph {optimism}\&\emph {adaptivity}. Leveraging the …

Reinforcement learning (RL) is a machine learning paradigm concerned with how an agent
learns to predict and control its own experience stream so as to maximize long-term …

We work towards a unifying paradigm for accelerating policy optimization methods in
reinforcement learning (RL) through predictive and adaptive directions of (functional) policy …