Jonathan P. How

Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning

2019 · arXiv (Cornell University)

We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors. Given the uncertainty in the intent of the other agent, the objective is to collect …

R-MADDPG for Partially Observable Environments and Limited Communication

2020 · arXiv (Cornell University)

There are several real-world tasks that would benefit from applying multiagent reinforcement learning (MARL) algorithms, including the coordination among self-driving cars. The real world has challenging conditions for multiagent learning systems, such as its partial …

Learning Hierarchical Teaching Policies for Cooperative Agents

2019 · arXiv (Cornell University)

Collective learning can be greatly enhanced when agents effectively exchange knowledge with their peers. In particular, recent work studying agents that learn to teach other teammates has demonstrated that action advising accelerates team-wide learning. However, …

Context-Specific Representation Abstraction for Deep Option Learning

2021 · arXiv (Cornell University)

Hierarchical reinforcement learning has focused on discovering temporally extended actions, such as options, that can provide benefits in problems requiring extensive exploration. One promising approach that learns these options end-to-end is the option-critic (OC) framework. …