Prompting Decision Transformers for Zero-Shot Reach-Avoid Policies

Abstract: Offline goal-conditioned reinforcement learning methods have shown promise for reach-avoid tasks, where an agent must reach a target state while avoiding undesirable regions of the state space. Existing approaches typically encode avoid-region information into an augmented state space and cost function, which prevents flexible, dynamic specification of novel avoid-region information at evaluation time. They also rely heavily on well-designed reward and cost functions, limiting scalability to complex or poorly structured environments. We introduce RADT, a decision transformer model for offline, reward-free, goal-conditioned, avoid region-conditioned RL. RADT encodes goals and avoid regions directly as prompt tokens, allowing any number of avoid regions of arbitrary size to be specified at evaluation time. Using only suboptimal offline trajectories from a random policy, RADT learns reach-avoid behavior through a novel combination of goal and avoid-region hindsight relabeling. We benchmark RADT against 3 existing offline goal-conditioned RL models across 11 tasks, environments, and experimental settings. RADT generalizes in a zero-shot manner to out-of-distribution avoid region sizes and counts, outperforming baselines that require retraining. In one such zero-shot setting, RADT achieves 35.7% improvement in normalized cost over the best retrained baseline while maintaining high goal-reaching success. We apply RADT to cell reprogramming in biology, where it reduces visits to undesirable intermediate gene expression states during trajectories to desired target states, despite stochastic transitions and discrete, structured state dynamics.

Prompting Decision Transformers for Zero-Shot Reach-Avoid Policies

The paper "Prompting Decision Transformers for Zero-Shot Reach-Avoid Policies" presents a novel approach to reinforcement learning (RL) for reach-avoid tasks, which are prevalent in autonomous systems, robotics, and biological applications. The focus is on developing a flexible, offline reinforcement learning model capable of generalizing to dynamically specified goals and avoid regions without requiring retraining or expert demonstrations.

The authors introduce the Reach-Avoid Decision Transformer (RADT), a decision transformer model that employs a prompt-based framework to condition on both reaching and avoiding behaviors. This framework facilitates zero-shot generalization across varying numbers and sizes of avoid regions by encoding these specifications directly into the model's prompts during evaluation. This design distinguishes RADT from conventional RL models, which typically rely on encoded avoid-region semantics and intricately crafted reward or cost functions.

Core Contributions

1. Prompting Framework: RADT utilizes a prompt structure that encodes goals and multiple avoid regions as tokens within the prompt sequence, decoupling these specifications from the state representation. This enables dynamic conditioning on arbitrary goals and avoids regions at test time, addressing key limitations of previous methods that hardcode such information into augmented state representations.
2. Hindsight Relabeling: A novel avoid-region hindsight relabeling strategy is introduced, allowing RADT to learn effective reach-avoid behaviors from suboptimal data, bypassing the need for manually configured reward signals or cost functions. This strategy leverages training data generated by random policies, facilitating learning from trajectories that might violate avoid constraints.
3. Zero-Shot Generalization: RADT is evaluated across benchmarks requiring it to adapt to unseen avoid-region sizes and counts without retraining. It outperforms existing baseline models, demonstrating a notable 35.7% improvement in normalized cost over the best retrained baseline in zero-shot settings, all while maintaining high success rates in goal-reaching tasks.

Implications and Extensions

The implications of RADT's approach are significant for both theoretical and practical advancements in RL. The reward-free, prompt-based framework simplifies the specification of complex, dynamic tasks, reducing reliance on expert-defined standards. This model has strong applicability in domains where online exploration is not feasible or safe, such as in cell reprogramming for therapeutic purposes. In these scenarios, RADT successfully avoids undesirable gene expression states, showcasing its potential for real-world deployment in bioengineering and autonomous vehicle navigation.

Future developments could involve extending RADT's prompt-based conditioning to more abstract or complex avoid-region geometries, enhancing its capability to deal with intricate environmental constraints. Moreover, optimizing computational efficiency and scalability could make RADT applicable to even broader classes of high-dimensional and sequential decision-making problems, solidifying its position as a versatile tool for developing safe, autonomous agents across domains.

Conclusion

The paper's contributions mark a pivotal movement towards more adaptable and interpretable reinforcement learning frameworks. RADT's ability to dynamically process and respond to distinct, user-specified constraints without retraining positions it as a robust model for general-purpose reach-avoid RL tasks. While challenges such as computational demands and handling complex avoid-region shapes remain, RADT sets a solid foundation for future research and applications in dynamically constrained environments.