In order to improve reproducibility, deep reinforcement learning (RL) has been adopting
better scientific practices such as standardized evaluation metrics and reporting. However …

Despite significant progress in challenging problems across various domains, applying state-
of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their …

Model-based reinforcement learning (MBRL) is widely seen as having the potential to be
significantly more sample efficient than model-free RL. However, research in model-based …

Many deep reinforcement learning algorithms contain inductive biases that sculpt the
agent's objective and its interface to the environment. These inductive biases can take many …

MushroomRL is an open-source Python library developed to simplify the process of
implementing and running Reinforcement Learning (RL) experiments. Compared to other …

Deep reinforcement learning (RL) has achieved great empirical successes in various
domains. However, the large search space of neural networks requires a large amount of …

Deep reinforcement learning (RL) agents trained in a limited set of environments tend to
suffer overfitting and fail to generalize to unseen testing environments. To improve their …

Reinforcement learning (RL) algorithms can successfully solve a wide range of problems
that we faced. Because of the Alpha Go against KeJie in 2017, the topic of RL has reached …

Deep reinforcement learning (RL) has achieved breakthrough results on many tasks, but
agents often fail to generalize beyond the environment they were trained in. As a result …

With the increasing need for handling large state and action spaces, general function
approximation has become a key technique in reinforcement learning (RL). In this paper, we …