Jianye Hao

FESR: A Framework for Eliciting Security Requirements Based on Integration of Common Criteria and Weakness Detection Formal Model

2017

It is critical and foremost to come up with the corresponding security requirements first which the following implementations are based on. However, previous security requirement elicitation work based on Common Criteria (CC) rarely addresses the …

Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning

2020

Generating diverse behaviors for game artificial intelligence (Game AI) has been long recognized as a challenging task in the game industry. Designing a Game AI with a satisfying behavioral characteristic (style) heavily depends on the …

Critic PI2: Master Continuous Planning via Policy Improvement with Path Integrals and Deep Actor-Critic Reinforcement Learning

2020 · arXiv (Cornell University)

Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods from AlphaGo to Muzero have enjoyed huge success in discrete domains, such as …

An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning

2020 · arXiv (Cornell University)

Transfer Learning has shown great potential to enhance single-agent Reinforcement Learning (RL) efficiency. Similarly, Multiagent RL (MARL) can also be accelerated if agents can share knowledge with each other. However, it remains a problem of …

EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model

2022 · arXiv (Cornell University)

Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks. Previous works focused on …

Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning

2023 · arXiv (Cornell University)

A key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL). However, constructing a standalone RL policy that maps perception to action directly encounters severe problems, chief among them being its lack of …