ATARI Game

Atari games serve as a benchmark for evaluating reinforcement learning (RL) algorithms, focusing on an agent's ability to learn complex strategies from raw pixel input and achieve high scores. Current research emphasizes improving sample efficiency and training speed through advancements in model architectures like transformers and soft actor-critic (SAC) algorithms, as well as exploring the use of multimodal large language models and incorporating additional information like instruction manuals. This research contributes significantly to the development of more efficient and robust RL agents with broader applications beyond gaming, such as robotics and other complex control tasks.