We show that neural networks trained by evolutionary reinforcement learning can enact
efficient molecular self-assembly protocols. Presented with molecular simulation trajectories …

We show how to bound and calculate the likelihood of dynamical large deviations using
evolutionary reinforcement learning. An agent, a stochastic model, propagates a continuous …

Future industrial automation systems are anticipated to be shaped by intelligent
technologies that allow for the adaptability of machines to the variations and uncertainties in …

Autonomous surface vessels are becoming increasingly important for water monitoring.
Their aim is to navigate rivers and lakes with limited intervention of human operators, to …

Our work is a simple extension of the paper" Exploration by Random Network Distillation"[1].
More in detail, we show how to efficiently combine Intrinsic Rewards with Experience Replay …

This paper is a report of our extensive experimentation, during the last two years, of deep
reinforcement techniques for training an agent to move in the dungeons of the famous …

MicroRacer is a simple, open source environment inspired by car racing especially meant
for the didactics of Deep Reinforcement Learning. The complexity of the environment has …

Roguelike games are a challenging environment for Reinforcement Learning (RL)
algorithms due to having to restart the game from the beginning when losing, randomized …

Reinforcement Learning (RL) is a branch of machine learning where an agent taking actions
in a given environment is supposed to learn an optimal behaviour by acquiring experience …

Designing autonomous agents that play video games is important and valuable for game
design, balancing, and testing. Video games are also preferable benchmark environments …