Attention-based Deep Reinforcement Learning for Multi-view Environments
This addresses the challenge of learning complex policies in partially observable multi-view environments for reinforcement learning applications, representing an incremental improvement.
The paper tackles the problem of partial observability in multi-view reinforcement learning by introducing an attention-based method that dynamically weights views based on their importance, achieving improved performance on TORCS and other 3D environments.
In reinforcement learning algorithms, it is a common practice to account for only a single view of the environment to make the desired decisions; however, utilizing multiple views of the environment can help to promote the learning of complicated policies. Since the views may frequently suffer from partial observability, their provided observation can have different levels of importance. In this paper, we present a novel attention-based deep reinforcement learning method in a multi-view environment in which each view can provide various representative information about the environment. Specifically, our method learns a policy to dynamically attend to views of the environment based on their importance in the decision-making process. We evaluate the performance of our method on TORCS racing car simulator and three other complex 3D environments with obstacles.