Mixed-initiative multiagent apprenticeship learning for human training of robot teams
Extending recent advances in Learning from Demonstration (LfD) frameworks to multi-robot
settings poses critical challenges such as environment non-stationarity due to partial …
settings poses critical challenges such as environment non-stationarity due to partial …
Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review
Reinforcement learning (RL) is a sub-domain of machine learning, mainly concerned with
solving sequential decision-making problems by a learning agent that interacts with the …
solving sequential decision-making problems by a learning agent that interacts with the …
Agent-centric representations for multi-agent reinforcement learning
Object-centric representations have recently enabled significant progress in tackling
relational reasoning tasks. By building a strong object-centric inductive bias into neural …
relational reasoning tasks. By building a strong object-centric inductive bias into neural …
[PDF][PDF] Dec-AIRL: Decentralized adversarial IRL for human-robot teaming
We present a new method for inverse reinforcement learning (IRL) that allows an agent to
learn from expert demonstrations and then spontaneously collaborate with a human on the …
learn from expert demonstrations and then spontaneously collaborate with a human on the …
Discovering individual rewards in collective behavior through inverse multi-agent reinforcement learning
The discovery of individual objectives in collective behavior of complex dynamical systems
such as fish schools and bacteria colonies is a long-standing challenge. Inverse …
such as fish schools and bacteria colonies is a long-standing challenge. Inverse …
[PDF][PDF] Latent Goal Allocation for Multi-Agent Goal-Conditioned Self-Supervised Imitation Learning
Multi-agent learning plays an essential role in ubiquitous practical applications including
game theory, autonomous driving, etc. On the other end, goal-conditioned learning attracts a …
game theory, autonomous driving, etc. On the other end, goal-conditioned learning attracts a …
Inverse reinforcement learning for multi-player apprentice games in continuous-time nonlinear systems
We extend the inverse reinforcement learning (inverse RL) algorithms to multi-player
apprentice games described by nonlinear differential equations. In these games, both the …
apprentice games described by nonlinear differential equations. In these games, both the …
Optimizing Mechanism Design in Multi-Agent Reinforcement Learning
DA Molina Concha - 2024 - utoronto.scholaris.ca
The increasing adoption of agentic systems for decentralized deployment of artificial
intelligence presents new challenges in efficiently selecting parameters that influence …
intelligence presents new challenges in efficiently selecting parameters that influence …
[图书][B] Coordination in Cooperative Multi-Agent Learning
P Barde - 2023 - search.proquest.com
Exploring the process by which autonomous agents coordinate represents a pivotal
advancement toward emulating populations, which encompasses diverse applications in …
advancement toward emulating populations, which encompasses diverse applications in …
Optimizing Mechanism Design in Multi-Agent Reinforcement Learning
DM Concha - 2024 - search.proquest.com
The increasing adoption of agentic systems for decentralized deployment of artificial
intelligence presents new challenges in efficiently selecting parameters that influence …
intelligence presents new challenges in efficiently selecting parameters that influence …