Visual Analogies between Atari Games for Studying Transfer Learning in RL
This work addresses transfer learning in reinforcement learning for video games, but it appears incremental as it builds on existing methods without claiming major breakthroughs.
The paper tackled the problem of transferring knowledge between Atari games using unsupervised visual analogies, demonstrating convincing visual mappings for four game pairs and evaluating three transfer learning approaches.
In this work, we ask the following question: Can visual analogies, learned in an unsupervised way, be used in order to transfer knowledge between pairs of games and even play one game using an agent trained for another game? We attempt to answer this research question by creating visual analogies between a pair of games: a source game and a target game. For example, given a video frame in the target game, we map it to an analogous state in the source game and then attempt to play using a trained policy learned for the source game. We demonstrate convincing visual mapping between four pairs of games (eight mappings), which are used to evaluate three transfer learning approaches.