Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents

RL game playing agents are traditionally initialized with zero pre-existing knowledge about a specific game environment and learn to play the game through millions of interactions with the environment. Significant time and compute is often spent exploring states that will not be experienced during high scoring policies. Exploration is particularly challenging in environments that require long horizon action sequences and provide sparse rewards, such as the Atari games and real-world robotics challenges where the state space is too large to effectively sample through free-form exploration. In this paper we will explore whether pretrained general RL agents like reasoning LLMs can play Atari games and investigate ways to leverage pretrained RL agents to reduce the training samples for training smaller agents from scratch. We first explore whether the contextual under-1 Stanford University.