226 citations · 1k across the 102 of their papers we have counts for
8 papers · 1 filter
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning
Vasu Sharma, Prasoon Goyal, Kaixiang Lin +3
We propose a multimodal (vision-and-language) benchmark for cooperative and heterogeneous multi-agent learning. We introduce a benchmark multimodal dataset with tasks involving col…
DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
Xiaofeng Gao, Qiaozi Gao, Ran Gong +3
Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural la…
LUMINOUS: Indoor Scene Generation for Embodied AI Challenges
Yizhou Zhao, Kaixiang Lin, Zhiwei Jia +4
Learning-based methods for training embodied agents typically require a large number of high-quality scenes that contain realistic layouts and support meaningful interactions. Howe…
Solving Markov Decision Processes with Reachability Characterization from Mean First Passage Times
Shoubhik Debnath, Lantao Liu, Gaurav Sukhatme
A new mechanism for efficiently solving the Markov decision processes (MDPs) is proposed in this paper. We introduce the notion of reachability landscape where we use the Mean Firs…
Reachability and Differential based Heuristics for Solving Markov Decision Processes
Shoubhik Debnath, Lantao Liu, Gaurav Sukhatme
The solution convergence of Markov Decision Processes (MDPs) can be accelerated by prioritized sweeping of states ranked by their potential impacts to other states. In this paper,…
Region Growing Curriculum Generation for Reinforcement Learning
Artem Molchanov, Karol Hausman, Stan Birchfield +1
Learning a policy capable of moving an agent between any two states in the environment is important for many robotics problems involving navigation and manipulation. Due to the spa…