Adaptive Transformers in RL (2004.03761v1)

Abstract: Recent developments in Transformers have opened new interesting areas of research in partially observable reinforcement learning tasks. Results from late 2019 showed that Transformers are able to outperform LSTMs on both memory intense and reactive tasks. In this work we first partially replicate the results shown in Stabilizing Transformers in RL on both reactive and memory based environments. We then show performance improvement coupled with reduced computation when adding adaptive attention span to this Stable Transformer on a challenging DMLab30 environment. The code for all our experiments and models is available at https://github.com/jerrodparker20/adaptive-transformers-in-rl.

An Analysis of Adaptive Transformers in Reinforcement Learning

The paper "Adaptive Transformers in RL" explores the application of adaptive attention mechanisms in Transformers applied to reinforcement learning (RL), particularly in environments where memory requirements vary. Through rigorous experimentation, the authors examine the effectiveness of adaptive attention span in Transformers, building on previous work of enhancing Transformer architectures for RL tasks.

Introduction and Background

Transformers have gained prominence in NLP due to their proficiency in modeling long-range dependencies through self-attention mechanisms. This paper investigates extending Transformers into RL domains, particularly in partially observable environments where agents must infer the current state using past observations, akin to language prediction tasks. Prior efforts with the TransformerXL (TXL) in reinforcement learning demonstrated potential improvements over LSTMs in RL tasks with memory dependencies. However, these models required constrained memory block sizes to remain computationally feasible, unlike their success in LLMs.

The primary thrust of this paper lies in integrating adaptive attention spans, a strategy that has shown promise in LLMing by enabling selective temporal focus in attention heads, potentially optimizing computational resources while maintaining robust memory length. The authors aim to validate if such architectural innovations can similarly enhance RL environments' task performance.

Experimentation and Results

The reported experiments span both reactive and memory-intensive environments, such as the Atari game Pong and DMLab30's rooms_select_nonmatching_object, respectively. These two setups test the model’s performance across distinct reinforcement learning spectrums: one favoring short-term reaction and the other demanding long-term memory recall.

The critical contribution of this work involves applying and evaluating adaptive attention spans within Stable Transformers—those modified with pre-processing steps to maintain stability in RL, as introduced in previous studies. Results from these experiments reveal:

1. Reactive Tasks (Atari Pong): Stable Transformers display a learning curve comparable to LSTMs for reactive tasks, achieving satisfactory performance with fewer parameters while LSTMs struggle with increased model complexities.
2. Memory-Based Tasks (DMLab30): In memory-intensive scenarios, the Adaptive Transformer exhibits superior performance over traditional Stable Transformers, achieving higher episode returns with greater stability. This advantageous outcome stems from adaptively managing long-term dependencies by optimizing attention spans per attention head.

The paper underscores the efficacy of adaptive attention, notably in memory-intensive tasks, where Adaptive Transformers not only matched but surpassed previous performance benchmarks set by other transformer-based models. Importantly, adaptive spans enabled a scalable memory management approach without proportionally increasing computational burden, an advantage over static attention mechanisms in TXL.

Implications and Future Directions

Practically, this paper broadens the horizons for computational efficiency in RL tasks utilizing Transformers with adaptive attention, highlighting mechanistic optimizations that cater to RL’s inherent sequential data characteristics. Theoretical implications suggest that attentively fine-tuned architectures can facilitate more sophisticated agents without the overhead of unwieldy parameter increments.

The authors acknowledge computational considerations as a limitation, suggesting that future work should incorporate extensive hyperparameter tuning over larger-scale tasks and environments. Potential explorations could include employing persistent memory mechanisms alongside adaptive spans or adopting gating techniques and expanding the suite of environments tested to encompass more broad-ranging cognitive benchmarks.

In essence, this paper provides a pivotal look into how evolving Transformer architectures, tuned with adaptive attention spans, can significantly benefit RL, paving the way for future enhancements in AI agent design where dynamic contextual modeling is pivotal.