History Compression via Language Models in Reinforcement Learning

Abstract: In a partially observable Markov decision process (POMDP), an agent typically uses a representation of the past to approximate the underlying MDP. We propose to utilize a frozen Pretrained Language Transformer (PLT) for history representation and compression to improve sample efficiency. To avoid training of the Transformer, we introduce FrozenHopfield, which automatically associates observations with pretrained token embeddings. To form these associations, a modern Hopfield network stores these token embeddings, which are retrieved by queries that are obtained by a random but fixed projection of observations. Our new method, HELM, enables actor-critic network architectures that contain a pretrained language Transformer for history representation as a memory module. Since a representation of the past need not be learned, HELM is much more sample efficient than competitors. On Minigrid and Procgen environments HELM achieves new state-of-the-art results. Our code is available at https://github.com/ml-jku/helm.

• The paper presents the FrozenHopfield mechanism integrated into the HELM framework to efficiently compress history in RL environments.
• The methodology leverages pretrained language models to map RL observations into a token embedding space without additional training, boosting sample efficiency.
• Empirical results demonstrate state-of-the-art performance in Minigrid, Procgen, and RandomMaze, highlighting the approach's scalability and robustness.

Overview of "History Compression via LLMs in Reinforcement Learning"

The paper, "History Compression via LLMs in Reinforcement Learning," addresses the challenge of effectively representing and compressing past observations in reinforcement learning (RL) environments, particularly within the context of partially observable Markov decision processes (POMDPs). The authors propose an innovative approach leveraging Pretrained Language Transformers (PLTs) for history representation, thus enhancing sample efficiency in actor-critic network architectures.

Key Contributions

1. FrozenHopfield Mechanism: A novel approach named FrozenHopfield is introduced, which circumvents the traditional need to train Transformers by mapping observations to a pretrained token embedding space using a modern Hopfield network. This associative memory mechanism effectively bridges the gap between RL observations and the language domain.
2. HELM Framework: The authors developed HELM, an actor-critic architecture integrating a PLT to streamline history representation. This network design capitalizes on the aforementioned FrozenHopfield mechanism and employs Transformers as memory modules to store environmental observations for subsequent decision-making processes.
3. Sample Efficiency: By utilizing pretrained models without additional training, the proposed framework significantly boosts sample efficiency compared to traditional methodologies. This efficiency is validated through empirical testing on Minigrid and Procgen environments, with HELM achieving state-of-the-art performance metrics.

Methodology

The methodology underpins a shift in how memory is managed in POMDP environments. By embedding observations using random projections and retrieving them through a Hopfield network, the authors effectively utilize the Transformer’s inherent capability for abstraction and compression to represent historical data. This process allows for the transformation of POMDPs into effectively manageable MDPs without additional learning requirements for history representation.

The FrozenHopfield mechanism operates by projecting observations into the Transformer’s token space via random yet fixed projections. These are then processed through the Transformer, which acts as a frozen memory module. As a result, the computational burden is minimized, and the system efficiently abstracts and recalls essential historical information necessary for optimal policy learning.

Empirical Evaluation

In the empirical section, the HELM framework demonstrates superior performance in terms of sample efficiency across several challenging environments:

• RandomMaze: HELM outperformed traditional and Markovian policies, showcasing the benefits of effective memory utilization.
• Minigrid: HelM outpaced LSTM-based methods, highlighting its utility in navigating complex POMDP environments.
• Procgen: HELM set new benchmarks across diverse memory-intensive tasks, proving the scalability and robustness of the approach.

Implications and Future Directions

The proposed approach suggests several implications for the future of AI and RL research:

• Increased Efficiency: The elimination of learning requirements for the memory representation stage opens new possibilities for deploying RL in resource-constrained scenarios.
• Transfer Learning: The success of using PLTs indicates potential pathways for incorporating more general pretrained architectures in RL settings, suggesting a robust symbiosis between LLMs and RL paradigms.

For future research, exploration into other domains of PLT applications within RL is promising. Investigating other memory architectures or further refining the integration of language-based models with RL can potentially accelerate progress in achieving more generalized AI systems. Additionally, adapting the techniques for environments with more complex state-action spaces or integrating multi-modal sensory data could be compelling areas of investigation.

In conclusion, the paper pioneers a methodological integration of LLMs into RL frameworks, offering novel perspectives on efficiently managing historical representations.