Learning and Querying Fast Generative Models for Reinforcement Learning (1802.03006v1)

Abstract: A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed generative models that learn and operate on compact state representations, so-called state-space models, substantially reduce the computational costs for predicting outcomes of sequences of actions. Extensive experiments establish that state-space models accurately capture the dynamics of Atari games from the Arcade Learning Environment from raw pixels. The computational speed-up of state-space models while maintaining high accuracy makes their application in RL feasible: We demonstrate that agents which query these models for decision making outperform strong model-free baselines on the game MSPACMAN, demonstrating the potential of using learned environment models for planning.

• The paper introduces state-space generative models that reduce computation 5x while maintaining high prediction accuracy.
• It demonstrates that integrating these models into RL architectures enhances decision-making over model-free baselines, as shown in challenging environments like Atari games.
• The paper explores strategic model querying through imaginative rollouts, enabling sample-efficient performance and robust planning in real-time applications.

Learning and Querying Fast Generative Models for Reinforcement Learning

The paper discussed here addresses a significant challenge in model-based reinforcement learning (RL): the development of computationally efficient and accurate environment models. The research introduces generative models that leverage compact state representations, known as state-space models, designed to minimize computational expenses while ensuring high prediction accuracy for action outcomes. The primary aim is to improve sample efficiency and performance in RL through explicit environment modeling, moving beyond the extensive experience requirements of model-free RL methods.

Core Contributions and Findings

The research makes several contributions that push forward the understanding and capabilities of generative models in RL:

1. Environment Modeling: The paper contrasts deterministic and stochastic models, specifically pixel-space and state-space models, on their speed and accuracy. The experiments, conducted across various challenging environments from the Arcade Learning Environment (ALE), demonstrate that state-space models can effectively capture environment dynamics like those in Atari games.
2. Computational Efficiency: State-space models significantly reduce computational demand by operating at a higher abstraction level compared to raw pixels. This leads to a speed-up factor exceeding five times over traditional autoregressive models, making them practical for applications requiring quick decision-making.
3. Accuracy with Stochastic Modeling: The stochastic state-space models are shown to produce diverse yet consistent rollouts, achieving state-of-the-art environment modeling accuracy, as evidenced by high log-likelihood scores in test domains.
4. Model-Based RL Performance: By integrating state-space models into RL architectures, specifically those involving the game MS_PACMAN, the paper shows improved performance over strong model-free baselines. The models enable agents to make better-informed decisions by systematically querying and leveraging predictions from learned models.
5. Learning to Query: The research examines the potential benefits of training agents to query models strategically through imaginative rollouts, aligning action and outcome predictions to boost decision-making accuracy.

Implications and Future Directions

The findings underscore the potential of state-space models to transform how RL systems learn and plan in complex environments. By efficiently modeling uncertainty and abstracting critical dynamics, these models offer a path toward more robust and sample-efficient RL algorithms.

Practically, the development of faster and more accurate environment models will be instrumental in deploying RL algorithms in real-time applications, such as robotics and autonomous systems, where decision latency and model fidelity are critical.

Theoretically, this research opens up avenues for exploring further abstractions in both space and time, potentially leading to models that adaptively learn temporal abstractions, thus reducing the model's computational burden while retaining predictive accuracy.

Future work may focus on the co-evolution of model learning and agent training within the same loop, thereby eliminating the need for pre-trained models. Additionally, exploring architectures that integrate adaptive abstract temporal representation will be crucial for advancing planning capabilities in RL.

Conclusion

The paper makes a compelling case for using generative state-space models in RL, highlighting their efficiency and robustness compared to traditional approaches. By addressing computational constraints while maintaining high accuracy, these models pave the way for more advanced, efficient, and capable RL systems.