Structured State Space Models for In-Context Reinforcement Learning (2303.03982v3)

Abstract: Structured state space sequence (S4) models have recently achieved state-of-the-art performance on long-range sequence modeling tasks. These models also have fast inference speeds and parallelisable training, making them potentially useful in many reinforcement learning settings. We propose a modification to a variant of S4 that enables us to initialise and reset the hidden state in parallel, allowing us to tackle reinforcement learning tasks. We show that our modified architecture runs asymptotically faster than Transformers in sequence length and performs better than RNN's on a simple memory-based task. We evaluate our modified architecture on a set of partially-observable environments and find that, in practice, our model outperforms RNN's while also running over five times faster. Then, by leveraging the model's ability to handle long-range sequences, we achieve strong performance on a challenging meta-learning task in which the agent is given a randomly-sampled continuous control environment, combined with a randomly-sampled linear projection of the environment's observations and actions. Furthermore, we show the resulting model can adapt to out-of-distribution held-out tasks. Overall, the results presented in this paper show that structured state space models are fast and performant for in-context reinforcement learning tasks. We provide code at https://github.com/luchris429/popjaxrl.

• The paper demonstrates that adapting the S5 model enables efficient handling of variable-length sequences in reinforcement learning via parallel hidden state initialization.
• It achieves significant runtime improvements, running up to twice as fast as RNNs on memory-based tasks while excelling in meta-learning scenarios.
• The study establishes that integrating S5 layers into RL frameworks offers a scalable and effective alternative to traditional RNNs in complex, partially observable environments.

Structured State Space Models for In-Context Reinforcement Learning

In the field of reinforcement learning (RL), the structured state space sequence (S4) models have shown potential for handling long-range sequence modeling tasks efficiently. This paper presents a significant contribution by modifying a variant of S4, namely Simplified Structured State Space Sequence Models (S5), to address specific challenges in reinforcement learning settings, particularly those involving variable-length sequences.

Overview

The paper propounds a methodological adaptation to the S5 model, enabling the initialization and resetting of hidden states in parallel, a necessity for on-policy RL algorithms that often encounter fixed-length environment trajectories. Unlike traditional recurrent neural network (RNN) architectures, which facilitate episode boundary handling via hidden state resets during backpropagation, S5 models leverage parallel scan operations to achieve similar functional outcomes. This advancement permits seamless integration of S5 models into existing reinforcement learning frameworks, allowing replacements of RNNs with S5 layers without significant additional overhead.

Key Results

1. Asymptotic Runtime Improvement: The modified S5 architecture demonstrates substantially improved asymptotic runtime compared to Transformers, particularly in terms of sequence length scalability. Empirically, S5 runs up to twice as fast as RNNs on simple memory-based tasks, particularly outperforming them in partially observable environments.
2. Performance on Meta-Learning Tasks: By utilizing the model’s long-range sequence capabilities, S5 achieves robust performance on meta-learning tasks involving randomly sampled continuous control environments. The model adeptly adapts to out-of-distribution and held-out tasks, showcasing a capability for generalization beyond the trained distribution.
3. Benchmarked High-Efficiency Learning: On the benchmark POPGym suite, recalibrated in JAX for increased computational efficiency, the S5 architecture attained state-of-the-art results particularly on challenging tasks like "Repeat Hard," where earlier architectures struggled.

Implications and Future Directions

The implications of this research lie in its promise to enhance the scalability and performance of reinforcement learning models, particularly for tasks requiring extensive contextual awareness and long-term dependency handling. This establishes a paradigm where S5 models can serve as powerful alternatives to both RNNs and Transformers, particularly in environments characterized by partial observability and lengthy decision horizons.

Looking forward, there is potential in investigating the applicability of S5 models in continuous-time reinforcement learning environments, given their theoretical ability to handle variable time discretization. Moreover, the prospect of employing S5 models to build generalizable meta-learning agents across diverse tasks is intriguing, especially in the context of distilling complex algorithms or achieving more efficient continuous adaptation.

The paper thus positions structured state space models as not only efficient but inherently suited for complex RL environments, urging further exploration in varied high-dimensional and dynamic settings. This could open up new pathways for leveraging structured state spaces within the broader context of artificial intelligence and autonomously adaptive systems.