In-Context Reinforcement Learning for Variable Action Spaces (2312.13327v6)

Abstract: Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context. A key limitation of previously proposed models is their reliance on a predefined action space size and structure. The introduction of a new action space often requires data re-collection and model re-training, which can be costly for some applications. In our work, we show that it is possible to mitigate this issue by proposing the Headless-AD model that, despite being trained only once, is capable of generalizing to discrete action spaces of variable size, semantic content and order. By experimenting with Bernoulli and contextual bandits, as well as a gridworld environment, we show that Headless-AD exhibits significant capability to generalize to action spaces it has never encountered, even outperforming specialized models trained for a specific set of actions on several environment configurations. Implementation is available at: https://github.com/corl-team/headless-ad.

In-Context Reinforcement Learning for Variable Action Spaces

Introduction

The paper presents a novel approach to reinforcement learning (RL) with variable action spaces using the transformer architecture. The authors address a key limitation in existing models, such as Algorithm Distillation (AD), which require predefined action space sizes and structures. This constraint necessitates data re-collection and model re-training for new action spaces, an impractical demand for many applications. The proposed Headless-AD model aims to mitigate this issue by decoupling the model from specific action space configurations, enhancing its adaptability to discrete action spaces of varying sizes, semantic contents, and orders.

Key Contributions

The paper makes several notable contributions:

1. Introduction of Headless-AD: By removing the final linear layer in AD, the Headless-AD model predicts action embeddings directly, avoiding fixed connections to specific action space sizes or structures.
2. Random Action Embeddings: Actions are encoded with random embeddings, dynamically generated at each training step, improving the model's robustness to new actions.
3. Action Set Prompt: The model input includes a sequence of potential action embeddings, aiding the model in understanding the action space structure during inference.

Experimental Validation

The effectiveness of Headless-AD was validated through various experiments involving different environments, including Bernoulli and contextual bandits, as well as a darkroom gridworld setup.

Bandit Experiments

1. Bernoulli Bandit with Changing Rewards: The model was tested for robustness against different reward distributions. The Headless-AD model matched the performance of traditional Thompson Sampling methods, demonstrating strong in-context learning (ICL) capabilities.
2. Scaling to Larger Action Spaces: Headless-AD maintained its performance across environments with 20 to 50 arms, showcasing its ability to handle large action spaces without requiring re-training, unlike AD.

Contextual Bandit

The experiments demonstrated that Headless-AD could match the performance of LinUCB across varying numbers of actions and generalize to new, unseen action spaces, further highlighting its robust ICL abilities.

Darkroom Gridworld

In this complex environment, the authors evaluated the adaptability of Headless-AD and AD to various new action spaces:

1. Permuted Train Actions: Headless-AD maintained performance with reordered action elements, unlike AD.
2. Test Actions and All Actions: Headless-AD adapted to completely new and larger action sets without retraining, a significant advantage over AD.
3. Evaluation with Different Goal Configurations: Both Headless-AD and AD exhibited ICL capabilities by maintaining performance on new tasks with changed goals. However, Headless-AD's superior adaptability to changing action spaces was evident.

Ablation Studies

The authors conducted ablation studies to underline the importance of the architectural choices in Headless-AD:

1. Action Set Prompt: The inclusion of the action embeddings in the model’s input sequence was critical for performance, especially in environments with larger action spaces.
2. Contrastive Loss: The use of InfoNCE contrastive loss, as opposed to direct copying incentivized by mean squared error (MSE) loss, was essential. Models trained with MSE exhibited reduced performance by converging to suboptimal actions due to limited exploration.

Implications and Future Directions

The research presented has several implications for both practical applications and theoretical advancements in RL. Practically, the versatility of Headless-AD in adapting to new action spaces without requiring re-training makes it suitable for large-scale pretraining across diverse environments. Theoretically, it points towards the potential of combining transformer architectures with robust exploration methods in RL.

The paper also opens several avenues for future research:

1. Extension to Continuous Action Spaces: While this paper focuses on discrete action spaces, future research could extend these ideas to continuous domains, possibly utilizing approximate nearest neighbor lookup for efficient action selection.
2. Broader Environment Testing: Further validation in more complex real-world environments could provide deeper insights into the model’s robustness and versatility.
3. Integration with Other Models: Assessing the compatibility of Headless-AD with models beyond AD, such as Decision Pretrained Transformer (DPT), would provide a more comprehensive understanding of its strengths and limitations.

Conclusion

The paper presents a significant advancement in the domain of RL with variable action spaces. By addressing the limitations of traditional approaches, Headless-AD demonstrates robust generalization capabilities, making substantial contributions to the development of versatile RL agents capable of tackling a wide range of dynamic environments. The insights from this work pave the way for further innovations in creating generalist models in reinforcement learning.