Jiayu Chen

Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning

2019 · arXiv (Cornell University)

Making decisions in complex driving environments is a challenging task for autonomous agents. Imitation learning methods have great potentials for achieving such a goal. Adversarial Inverse Reinforcement Learning (AIRL) is one of the state-of-art imitation …

Multi-agent Deep Covering Skill Discovery

2022 · arXiv (Cornell University)

The use of skills (a.k.a., 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 …

Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control

2023

Hierarchical Imitation Learning (HIL) has been proposed to recover highly-complex behaviors in long-horizon tasks from expert demonstrations by modeling the task hierarchy with the option framework. Existing methods either overlook the causal relationship between the …

Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries

2024 · arXiv (Cornell University)

Federated Reinforcement Learning (FRL) allows multiple agents to collaboratively build a decision making policy without sharing raw trajectories. However, if a small fraction of these agents are adversarial, it can lead to catastrophic results. We …