Atari Benchmark

The Atari benchmark, using the Arcade Learning Environment (ALE), assesses the capabilities of deep reinforcement learning (RL) agents across diverse Atari 2600 games. Current research focuses on improving sample efficiency and addressing limitations in existing algorithms, such as variance in value estimation and the exploration-exploitation dilemma, often employing ensemble methods and advanced planning techniques like Monte Carlo Tree Search. These efforts aim to push RL agents towards achieving truly superhuman performance, measured against human world records, thereby advancing the field's understanding of general-purpose intelligence and providing valuable insights for broader RL applications.