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Task Independent Capsule-Based Agents for Deep Q-Learning

In recent years, Capsule Networks (CapsNets) have achieved promising results in tasks such as object recognition thanks to their invariance characteristics towards pose and lighting. They have been proposed as an alternative to relational insensitive and translation invariant Convolutional Neural Networks (CNN). It has been empirically proven that CapsNets are capable of achieving competitive performance while requiring significantly fewer parameters. This is a desirable characteristic for Deep reinforcement learning which is known to be sample-inefficient during training. In this paper, we propose DCapsQN, a task-independent CapsNets-based architecture in the deep reinforcement learning setting. We experiment in the model-free reinforcement learning setting, more specifically in Deep Q-Learning using the Atari suite as the testbed of our analysis. To the best of our knowledge, this work constitutes the first CapsNets-based deep reinforcement learning architecture to learn state-action value functions without the need for task-specific adaptation. Our results show that, in this setting, DCapsQN requires 92% fewer parameters than the baseline. Moreover, despite their smaller size, the DCapsQN provides significant boosts in performance (score), ranging between 10%–77% while further stabilizing the Deep Q-Learning. This is supported by our empirical results which shows that DCapsQN agents outperform the benchmark Double-DQN agent, with Prioritized experience replay, in eight out of the nine selected environments.

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