A Survey of In-Context Reinforcement Learning
This paper presents an extensive survey of In-Context Reinforcement Learning (ICRL), a novel area within the broader field of reinforcement learning (RL) that explores agents' capability to solve new tasks using contextual information without explicitly updating network parameters. Researchers Moeini et al. detail advancements in this domain, categorizing existing work into supervised and reinforcement pretraining methods, and further dissecting test-time performance, context construction, and theoretical underpinnings.
ICRL stands apart by allowing pretrained RL agents to efficiently adapt to new environments using merely a forward pass in the network, which processes accumulated context—such as past observations and actions—without requiring expensive backward passes for parameter updates. This mechanism is posited to emerge from the network implementing an RL algorithm through its forward pass, which can facilitate in-context improvement as task-related context builds up. The authors emphasize the significance of this approach in reducing computational and memory burdens, potentially enhancing sample efficiency and enabling agents to generalize across a broader spectrum of environments.
The paper categorizes pretraining methodologies into supervised and reinforcement pretraining. Supervised pretraining often involves behavior cloning, where the pretraining objective typically involves maximizing the likelihood of reproducing expert actions given specific state-context pairs. This contrasts with traditional RL as it requires the policy to rely on a history extended beyond single trajectories. The authors suggest that successful supervised approaches leverage multiepisode context and curriculum strategies to catalyze in-context policy improvement.
Reinforcement pretraining, distinct from its supervised counterpart, utilizes established RL algorithms to conditionally adapt policies based on extended histories. Recent works marked improvements over early ICRL attempts by demonstrating broader out-of-distribution generalization in various synthetic benchmarks. These outcomes are consequent upon harnessing long-context neural architectures, such as transformers, to stabilize learning across diverse tasks. However, elucidating why these architectures enable broader generalization remains a compelling area for further investigation.
Moreover, the paper explores the strategies for context construction and subsequent test-time deployment. Famous challenges include dealing with unavailable RTG during testing or the reliance on expert demonstrations—an aspect that often hampers generalization and sample efficiency. The authors introduce frameworks and algorithms that target these challenges, enhancing test-time adaptation by using learned contexts effectively.
In test-time performance, the paper underlines the formidable out-of-distribution generalization exhibited by ICRL across landmarks benchmarks like Dark Room, Procgen, and XLand 2.0. This promising capacity reflects both contextual learning improvements and robust sample efficiency, although practical execution under sparse reward settings and long horizons remains an ongoing concern.
Theoretical inquiries into ICRL, albeit nascent, offer profound insights into how learning algorithms might intrinsically align with RL behavior through appropriately parameterized neural networks. Works exploring regret minimization and temporal difference methods hint at the potential for RL paradigms to be reliably embedded within the forward pass of neural networks, raising pivotal questions about the broader implications of such embeddings in practical applications.
Overall, this survey underscores the intersection of theoretical exploration and empirical achievement within the ICRL sector. Despite its relative youth, ICRL presents ample opportunities for addressing fundamental challenges in multi-agent scenarios and robotics, especially regarding generalization across unseen tasks and environments. The authors call for more refined theoretical models and empirical strategies to thoroughly delineate and harness the emergent properties of ICRL.
The authors conclude by acknowledging ICRL as a fertile ground for future research, proposing the formal white-boxing of its emergent behavior during reinforcement pretraining as a prospective area for innovation. As the field progresses, the ability of agents to self-adapt through transparent, context-driven mechanisms may profoundly transform the design and application of intelligent systems in real-world scenarios.