Learning to Act by Predicting the Future

Abstract: We present an approach to sensorimotor control in immersive environments. Our approach utilizes a high-dimensional sensory stream and a lower-dimensional measurement stream. The cotemporal structure of these streams provides a rich supervisory signal, which enables training a sensorimotor control model by interacting with the environment. The model is trained using supervised learning techniques, but without extraneous supervision. It learns to act based on raw sensory input from a complex three-dimensional environment. The presented formulation enables learning without a fixed goal at training time, and pursuing dynamically changing goals at test time. We conduct extensive experiments in three-dimensional simulations based on the classical first-person game Doom. The results demonstrate that the presented approach outperforms sophisticated prior formulations, particularly on challenging tasks. The results also show that trained models successfully generalize across environments and goals. A model trained using the presented approach won the Full Deathmatch track of the Visual Doom AI Competition, which was held in previously unseen environments.

• The paper introduces DFP, a method that predicts future measurement outcomes, replacing sparse rewards with dense, vectorial feedback.
• The approach leverages separate sensory and measurement streams to forecast action effects, significantly outperforming traditional deep RL in complex environments.
• Experimental results in Doom validate the model's robustness and adaptability in achieving dynamic goals through enriched sensorimotor control.

Overview of "Learning to Act by Predicting the Future"

This paper, authored by Alexey Dosovitskiy and Vladlen Koltun, explores an innovative approach to sensorimotor control in immersive environments through a method that leverages future prediction instead of the traditional reinforcement learning paradigm. The proposed methodology, termed Direct Future Prediction (DFP), employs high-dimensional sensory streams combined with lower-dimensional measurement streams to create a rich supervisory signal. This facilitates the training of sensorimotor control models by simply interacting with the environment, eschewing the typical extraneous supervision seen in conventional reinforcement learning.

Core Contributions

The authors present a paradigm shift in approaching sensorimotor control whereby the conventional RL framework of sparse scalar rewards is replaced with a dense, cotemporal structure between sensory and measurement streams. The method's efficacy is demonstrated in the first-person game Doom, with results showing significant performance improvements, particularly on complex tasks, over previous models. Notably, the model showcases robustness by generalizing well across different environments and goals, as evidenced by its performance in the Visual Doom AI Competition's Full Deathmatch track, achieving victory in previously unseen scenarios.

Methodology

The paper's key innovation lies in deconstructing the problem of sensorimotor control into predicting the effects of actions on future measurements, thereby recasting it as a supervised learning problem. This is achieved by focusing on the temporal interrelationship of sensory input and agent-centric measurements. By predicting the impact of current actions on future environmental states as encapsulated by these measurements, the model obviates the need for reward signals. This allows it to support dynamically changing goals at test time, distinguishing it from the monolithic goals characteristic of RL.

The architecture employs a deep network model that processes sensory and measurement inputs separately before integrating them for action prediction, utilizing separate streams for overall expectations and action-specific predictions. This structural bifurcation enhances the model’s ability to capture distinct outcomes associated with different actions.

Experimental Validation

The capacity of the proposed DFP model was rigorously tested in four Doom scenarios of escalating complexity. The model was compared against renowned deep RL approaches such as DQN, A3C, and DSR. Impressively, the DFP model consistently surpassed these methods in all but the simplest scenario, showcasing a particular edge in tasks involving greater complexity and requiring more sophisticated strategizing.

A key insight derived from the experiments is the pronounced advantage gained from the use of vectorial feedback, which contrasts with the scalar approach of traditional RL, bolstering training and enabling rich goal-driven control.

Implications and Future Directions

The findings and methodologies of this paper hold several implications for both current practice and future research in AI-driven sensorimotor learning. By shifting from reward-based strategies to a model rooted in supervised prediction, this work paves the way for more resilient and adaptive AI systems capable of functionally rich and dynamically flexible operations in complex environments. This model opens avenues for further exploration into combining DFP with memory architectures and expanding its applicability to continuous action spaces, thus covering gaps that remain outside the current model’s reach.

Additionally, while the model vindicates its approach within the discrete confines of a gaming environment, its principles suggest broader applicability in real-world robotics and AI, where sensory and feedback variance are the norms.

In conclusion, "Learning to Act by Predicting the Future" establishes a promising framework that challenges and extends beyond traditional methodologies, inviting further exploration into its broad applicability and potential integration with other AI paradigms.