Search Results for author:
Found 6 papers, 3 papers with code
Deep Multi-Agent Reinforcement Learning with Relevance Graphs
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data
In this paper, we demonstrate how data from videos of human running (e. g. taken from YouTube) can be used to shape the reward of the humanoid learning agent to speed up the learning and produce a better result.
End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box
The task object tracking is vital in numerous applications such as autonomous driving, intelligent surveillance, robotics, etc.
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold'em poker, which has a significantly smaller game tree (on the order of $10^{164}$ nodes).
