2 citations · 8 across the 12 of their papers we have counts for
5 papers · 1 filter
Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning
Chulabhaya Wijesundara, Andrea Baisero, Zhongheng Li +3
Centralized training with decentralized execution (CTDE) is a standard framework for cooperative multi-agent policy-gradient reinforcement learning, allowing agents to learn from j…
Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning
Andrea Baisero, Rupali Bhati, Shuo Liu +2
Value function decomposition methods for cooperative multi-agent reinforcement learning compose joint values from individual per-agent utilities, and train them using a joint objec…
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning
Xueguang Lyu, Andrea Baisero, Yuchen Xiao +1
Centralized Training for Decentralized Execution, where training is done in a centralized offline fashion, has become a popular solution paradigm in Multi-Agent Reinforcement Learn…
Unbiased Asymmetric Reinforcement Learning under Partial Observability
Andrea Baisero, Christopher Amato
In partially observable reinforcement learning, offline training gives access to latent information which is not available during online training and/or execution, such as the syst…
On a Family of Decomposable Kernels on Sequences
Andrea Baisero, Florian T. Pokorny, Carl Henrik Ek
In many applications data is naturally presented in terms of orderings of some basic elements or symbols. Reasoning about such data requires a notion of similarity capable of handl…