Emergence of In-Context Reinforcement Learning from Noise Distillation (2312.12275v3)

Abstract: Recently, extensive studies in Reinforcement Learning have been carried out on the ability of transformers to adapt in-context to various environments and tasks. Current in-context RL methods are limited by their strict requirements for data, which needs to be generated by RL agents or labeled with actions from an optimal policy. In order to address this prevalent problem, we propose AD$^\varepsilon$, a new data acquisition approach that enables in-context Reinforcement Learning from noise-induced curriculum. We show that it is viable to construct a synthetic noise injection curriculum which helps to obtain learning histories. Moreover, we experimentally demonstrate that it is possible to alleviate the need for generation using optimal policies, with in-context RL still able to outperform the best suboptimal policy in a learning dataset by a 2x margin.

Emergence of In-Context Reinforcement Learning from Noise Distillation

The paper "Emergence of In-Context Reinforcement Learning from Noise Distillation" by Zisman et al. explores the challenges and innovative approaches in achieving in-context reinforcement learning (RL) using transformers. The authors address the limitations of current in-context RL methods, notably the stringent data requirements either derived from RL agents or labeled with optimal policies. To overcome these limitations, they introduce a novel approach titled AD$^\varepsilon$, which leverages a noise-induced curriculum to facilitate data acquisition for in-context reinforcement learning (ICRL).

Overview of In-Context RL and Challenges

The paradigm of meta-reinforcement learning aims to teach agents to learn and adapt to new tasks efficiently. Recent advancements have attempted to achieve this through in-context learning, where transformers learn from a curated set of task interactions. However, existing methods face significant hurdles due to the requirement of extensive datasets that capture the trajectory of learning a task, such as Algorithm Distillation (AD) and Decision Pretrained Transformer (DPT).

1. Algorithm Distillation (AD): This method requires the collection of learning histories from thousands of single-task RL agents. These histories contain trajectories reflecting a spectrum of policy effectiveness. This requirement poses practical constraints due to the significant computational and temporal overhead involved.
2. Decision Pretrained Transformer (DPT): DPT requires optimal actions for pretraining, which might not always be feasible, especially in complex environments where determining the optimal policy is intractable.

AD$^\varepsilon$: An Innovative Approach for Data Acquisition

AD$^\varepsilon$ introduces a synthetic noise injection curriculum to construct data that enables ICRL without the need for numerous RL agents or optimal policy identification. The methodology involves adding controlled noise to a baseline policy to generate trajectories that mimic learning processes.

• Noise-Induced Data Generation: By progressively reducing the noise introduced in action selection, AD$^\varepsilon$ simulates the progression of learning. The curriculum ensures that data features an improvement pattern analogous to learning progressions found in real RL-generated data.
• Learning from Suboptimal Policies: AD$^\varepsilon$ extends its capabilities by demonstrating that in-context RL can learn effectively from suboptimal data, significantly outperforming the baseline policies used to generate the training data.

Experimental Validation and Key Findings

The experiments conducted across various environments such as grid-world tasks and complex 3D environments (e.g., Watermaze) reveal the robustness of AD$^\varepsilon$.

• Improved Performance: In-context learners trained using AD$^\varepsilon$ consistently outperform the best available policies in the training data by twofold or more.
• Robustness to Suboptimality: The method demonstrates that ICRL agents can enhance policies derived from suboptimal data, contradicting the presumption that near-optimal policies are essential for effective training.
• Importance of Learning Pace: The pace at which policies improve in the generated data plays a crucial role in the effectiveness of in-context RL, with optimal results emerging from a balanced progression rate.

Implications for Reinforcement Learning

The findings suggest that it is feasible to democratize data acquisition for in-context RL, reducing the dependency on exhaustive RL agent training and optimal policies. This advancement has profound implications for developing generalist RL agents capable of adapting across a diverse array of tasks with minimal data dependencies.

Future Prospects

The research sets the stage for further exploration into curriculum design and its impact on emergent in-context learning capabilities. The potential for broader application in varying environments highlights the necessity for continued investigation into the interactions between task complexity, data generation strategies, and learning performance. Subsequent studies could delve into optimizing the decay schedule of noise and exploring more complex, real-world tasks to further validate and enhance the method's applicability.