Recent spectacular progress in computational technologies has led to an unprecedented
boom in the field of Artificial Intelligence (AI). AI is now used in a plethora of research areas …

Procedural content generation in video games has a long history. Existing procedural
content generation methods, such as search-based, solver-based, rule-based and grammar …

The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …

Abstract We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like
environments designed to benchmark both sample efficiency and generalization in …

Training generally-capable agents with reinforcement learning (RL) remains a significant
challenge. A promising avenue for improving the robustness of RL agents is through the use …

To make deliberate progress towards more intelligent and more human-like artificial
systems, we need to be following an appropriate feedback signal: we need to be able to …

We investigate how reinforcement learning can be used to train level-designing agents. This
represents a new approach to procedural content generation in games, where level design …

Deep reinforcement learning (RL) has shown impressive results in a variety of domains,
learning directly from high-dimensional sensory streams. However, when neural networks …

Procedural content generation (PCG) refers to the practice of generating game content, such
as levels, quests or characters, algorithmically. Motivated by the need to make games …

Progress in deep reinforcement learning (RL) is heavily driven by the availability of
challenging benchmarks used for training agents. However, benchmarks that are widely …