Mastering Memory Tasks with World Models (2403.04253v1)

Abstract: Current model-based reinforcement learning (MBRL) agents struggle with long-term dependencies. This limits their ability to effectively solve tasks involving extended time gaps between actions and outcomes, or tasks demanding the recalling of distant observations to inform current actions. To improve temporal coherence, we integrate a new family of state space models (SSMs) in world models of MBRL agents to present a new method, Recall to Imagine (R2I). This integration aims to enhance both long-term memory and long-horizon credit assignment. Through a diverse set of illustrative tasks, we systematically demonstrate that R2I not only establishes a new state-of-the-art for challenging memory and credit assignment RL tasks, such as BSuite and POPGym, but also showcases superhuman performance in the complex memory domain of Memory Maze. At the same time, it upholds comparable performance in classic RL tasks, such as Atari and DMC, suggesting the generality of our method. We also show that R2I is faster than the state-of-the-art MBRL method, DreamerV3, resulting in faster wall-time convergence.

• The paper presents the novel Recall to Imagine (R2I) method that integrates structured state space models with DreamerV3 to improve long-term memory and planning in reinforcement learning.
• The approach employs a parallel scan with an S4 variant to efficiently process long sequences, overcoming traditional RNN and transformer limitations.
• Empirical results on benchmarks like POPGym and Memory Maze show R2I’s superior performance, even surpassing human-level results in challenging 3D environments.

Insightful Overview of "Mastering Memory Tasks with World Models"

The paper "Mastering Memory Tasks with World Models" outlines the development of a novel method termed Recall to Imagine (R2I), which is a model-based reinforcement learning (MBRL) approach. This method focuses on endowing reinforcement learning agents with enhanced memory capabilities by leveraging structured state space models (SSMs) in a world model context. The primary innovation lies in the integration of SSMs with the DreamerV3 world model architecture, a leading MBRL framework, to create an agent capable of resolving complex tasks requiring long-term memory and credit assignment.

Methodological Developments

The proposed R2I method addresses key challenges in model-based reinforcement learning, specifically in managing long-range dependencies and ensuring computational efficiency. The authors utilize a variant of the S4 model within their world model framework, which benefits from the SSMs' ability to learn dependencies over long sequences through efficient parallel computation. This substitution addresses the shortcomings of traditional recurrent neural networks (RNNs) and transformers when handling extended temporal relationships due to RNNs' vanishing gradients and the transformers' quadratic complexity with respect to sequence length.

Key to the successful application of SSMs in R2I is the methodological choice of employing parallel scan, which allows simultaneous processing of sequences, thus improving training speed and supporting the retention of historical information. This contrasts with convolutional approaches and facilitates the handling of sequence resets necessary for reinforcement learning paradigms.

Empirical Evaluation

The R2I model is subjected to rigorous evaluation across several benchmarks that stress memory and credit assignment, including BSuite, POPGym, and the Memory Maze domain. In these tests, R2I demonstrates superior performance over existing baselines and, notably, surpasses human-level performance in some challenging 3D environments. This underscores the efficacy of SSMs in addressing POMDP challenges by efficiently encoding and utilizing long-term dependencies.

The experimental results also illustrate that R2I maintains competitive performance in standard reinforcement learning benchmarks like Atari 100K and DMC, ensuring that the enhancements in memory do not compromise general performance across a diverse array of tasks. This aspect of maintaining the generality of R2I positions it as a versatile model for real-world applications where tasks may vary widely in terms of their memory and processing requirements.

Implications and Future Directions

The integration of structured state space models into world models represents a significant methodological advance in reinforcement learning, particularly in tasks requiring extensive temporal reasoning. This development opens avenues for research on hybrid architectures that might further combine the strengths of SSMs and attention mechanisms, potentially leading to even more powerful models.

The work also suggests potential avenues for extending the depth of world models to accommodate longer sequences, which might further enhance the ability to solve tasks with extreme long-range dependencies. Future research may focus on optimizing the balance between model complexity and computational efficiency to maintain scalability while enhancing memory capabilities.

In conclusion, the paper contributes a sophisticated reinforcement learning framework that effectively marries the scalability of SSMs with the structured planning capabilities of DreamerV3, setting a new benchmark in environments where both memory and planning are critical.