Visual Transfer between Atari Games using Competitive Reinforcement Learning

TL;DR

This paper presents a novel method for transferring knowledge between Atari games using deep reinforcement learning with visual mapping, improving performance and data efficiency without fine-tuning pre-trained models.

Contribution

It introduces a learning approach that updates models with multiple agents trained on visual representations, enhancing transfer learning in Atari games.

Findings

Improved performance on target Atari games

Enhanced data efficiency in training

Greater stability in transfer learning processes

Abstract

This paper explores the use of deep reinforcement learning agents to transfer knowledge from one environment to another. More specifically, the method takes advantage of asynchronous advantage actor critic (A3C) architecture to generalize a target game using an agent trained on a source game in Atari. Instead of fine-tuning a pre-trained model for the target game, we propose a learning approach to update the model using multiple agents trained in parallel with different representations of the target game. Visual mapping between video sequences of transfer pairs is used to derive new representations of the target game; training on these visual representations of the target game improves model updates in terms of performance, data efficiency and stability. In order to demonstrate the functionality of the architecture, Atari games Pong-v0 and Breakout-v0 are being used from the OpenAI gym environment; as the source and target environment.