Jiayu Chen

Carnegie Mellon University | CMU · School of Computer Science

Skills are effective temporal abstractions established for sequential decision making tasks, which enable efficient hierarchical learning for long-horizon tasks and facilitate multi-task learning through their transferability. Despite extensive research, research gaps remain in multi-agent scenarios, particularly for automatically extracting subgro...

Sepsis, a life-threatening condition triggered by the body's exaggerated response to infection, demands urgent intervention to prevent severe complications. Existing machine learning methods for managing sepsis struggle in offline scenarios, exhibiting suboptimal performance with survival rates below 50%. This paper introduces the PosNegDM — “Rei...

Deep generative models (DGMs) have demonstrated great success across various domains, particularly in generating texts, images, and videos using models trained from offline data. Similarly, data-driven decision-making and robotic control also necessitate learning a generator function from the offline data to serve as the strategy or policy. In this...

Online optimization of resource management for large-scale data centers and infrastructures to meet dynamic capacity reservation demands and various practical constraints (e.g., feasibility and robustness) is a very challenging problem. Mixed Integer Programming (MIP) approaches suffer from recognized limitations in such a dynamic environment, whil...

The integration of Artificial Intelligence (AI) in mobile computing has brought significant advancements in creating user-centric applications and interfaces that enhance user experience, promote accessibility, and facilitate personalized interactions. This paper explores the influence of AI on mobile computing from a human factors perspective, emp...

Imitation learning (IL) has been proposed to recover the expert policy from demonstrations. However, it would be difficult to learn a single monolithic policy for highly complex long-horizon tasks of which the expert policy usually contains subtask hierarchies. Therefore, hierarchical IL (HIL) has been developed to learn a hierarchical policy from...

Covering skill (a.k.a., option) discovery has been developed to improve the exploration of RL in single-agent scenarios with sparse reward signals, through connecting the most distant states in the embedding space provided by the Fiedler vector of the state transition graph. Given that joint state space grows exponentially with the number of agents...

This paper addresses the important need for advanced techniques in continuously allocating workloads on shared infrastructures in data centers, a problem arising due to the growing popularity and scale of cloud computing. It particularly emphasizes the scarcity of research ensuring guaranteed capacity in capacity reservations during large-scale fai...

Imperfect Information Games (IIGs) offer robust models for scenarios where decision-makers face uncertainty or lack complete information. Counterfactual Regret Minimization (CFR) has been one of the most successful family of algorithms for tackling IIGs. The integration of skill-based strategy learning with CFR could potentially enhance learning pe...

Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a distribution of tasks based on multi-task expert demonstrations, which is essential for general-purpose robots. Existing MIL algorithms suffer from low data efficiency and poor performance on complex long-horizontal tasks. We develop Multi-task Hierarchical Adversari...

Learning rich skills through temporal abstractions without supervision of external rewards is at the frontier of Reinforcement Learning research. Existing works mainly fall into two distinctive categories: variational and Laplacian-based option discovery. The former maximizes the diversity of the discovered options through a mutual information loss...

The use of options can greatly accelerate exploration in reinforcement learning, especially when only sparse reward signals are available. While option discovery methods have been proposed for individual agents, in multi-agent reinforcement learning settings, discovering collaborative options that can coordinate the behavior of multiple agents and...

Hierarchical Imitation Learning (HIL) has been proposed to recover highly-complex behaviors in long-horizontal tasks from expert demonstrations by modeling the task hierarchy with the option framework. Existing methods either overlook the causal relationship between the subtask and its corresponding policy or fail to learn the policy in an end-to-e...

Covering option discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios with sparse reward signals, through connecting the most distant states in the embedding space provided by the Fiedler vector of the state transition graph. However, these option discovery methods cannot be directly extended to...

Covering option discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios, where only sparse reward signals are available. It aims to connect the most distant states identified through the Fiedler vector of the state transition graph. However, the approach cannot be directly extended to multi-agent...

With the freight delivery demands and shipping costs increasing rapidly, intelligent control of fleets to enable efficient and cost-conscious solutions becomes an important problem. In this paper, we propose DeepFreight, a model-free deep-reinforcement-learning-based algorithm for multi-transfer freight delivery, which includes two closely-collabor...

With the freight delivery demands and shipping costs increasing rapidly, intelligent control of fleets to enable efficient and cost-conscious solutions becomes an important problem. In this paper, we propose DeepFreight, a model-free deep-reinforcement-learning-based algorithm for multi-transfer freight delivery, which includes two closely-collabor...