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Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains
To tackle this challenge, we propose a game-theoretic, privacy-preserving mechanism, utilizing a secure multi-party computation (MPC) framework in MARL settings.
Multi-agent Reinforcement Learning
Policy Gradient Methods
+2
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula
Autocurricular training is an important sub-area of multi-agent reinforcement learning~(MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving scheme.
Benchmarking Robot Manipulation with the Rubik's Cube
Benchmarks for robot manipulation are crucial to measuring progress in the field, yet there are few benchmarks that demonstrate critical manipulation skills, possess standardized metrics, and can be attempted by a wide array of robot platforms.
Motivating Physical Activity via Competitive Human-Robot Interaction
This project aims to motivate research in competitive human-robot interaction by creating a robot competitor that can challenge human users in certain scenarios such as physical exercise and games.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
