Deep Apprenticeship Learning for Playing Games
This addresses the challenge of learning complex tasks in environments where reward signals are unavailable, though it is incremental as it builds on existing supervised learning techniques in reinforcement learning.
The authors tackled the problem of teaching AI agents to play Atari games without access to reward functions by proposing a novel apprenticeship learning method based on expert behavior. They demonstrated the approach's potential for future strong performance, though current results did not match state-of-the-art reinforcement learning benchmarks.
In the last decade, deep learning has achieved great success in machine learning tasks where the input data is represented with different levels of abstractions. Driven by the recent research in reinforcement learning using deep neural networks, we explore the feasibility of designing a learning model based on expert behaviour for complex, multidimensional tasks where reward function is not available. We propose a novel method for apprenticeship learning based on the previous research on supervised learning techniques in reinforcement learning. Our method is applied to video frames from Atari games in order to teach an artificial agent to play those games. Even though the reported results are not comparable with the state-of-the-art results in reinforcement learning, we demonstrate that such an approach has the potential to achieve strong performance in the future and is worthwhile for further research.