The central question addressed in this research is” can we define evaluation methodologies
that encourage reinforcement-learning (RL) algorithms to work effectively with real-life …

Performance evaluations are critical for quantifying algorithmic advances in reinforcement
learning. Recent reproducibility analyses have shown that reported performance results are …

Novel reinforcement learning algorithms, or improvements on existing ones, are commonly
justified by evaluating their performance on benchmark environments and are compared to …

Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior–how an
agent can learn to make good decisions given experience and rewards in a stochastic …

Reinforcement learning (RL) has recently achieved tremendous success in solving complex
tasks. Careful considerations are made towards reproducible research in machine learning …

Reinforcement Learning (RL) is a very complex domain and software for RL is
correspondingly complex. We analyse the scope, requirements, and potential for RL …

Many empirical results in reinforcement learning are based on a very small set of
environments. These results often represent the best algorithm parameters that were found …

. This article reports on the reinforcement learning competitions, which have been held
annually since 2006. In these events, researchers from around the world developed …

Many applications in Reinforcement Learning (RL) usually have noise or stochasticity
present in the environment. Beyond their impact on learning, these uncertainties lead the …

We introduce a standard framework for benchmarking in reinforcement learning. Bench
marks facilitate the comparison of alternative algorithms and can greatly accelerate research …