2 citations · 8 across the 12 of their papers we have counts for
13 papers
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…
On Stateful Value Factorization in Multi-Agent Reinforcement Learning
Enrico Marchesini, Andrea Baisero, Rupali Bhati +1
Value factorization is a popular paradigm for designing scalable multi-agent reinforcement learning algorithms. However, current factorization methods make choices without full jus…
On Centralized Critics in Multi-Agent Reinforcement Learning
Xueguang Lyu, Andrea Baisero, Yuchen Xiao +2
Centralized Training for Decentralized Execution where agents are trained offline in a centralized fashion and execute online in a decentralized manner, has become a popular approa…
Equivariant Reinforcement Learning under Partial Observability
Hai Nguyen, Andrea Baisero, David Klee +3
Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions. This paper identifies partially observable d…
Leveraging Fully Observable Policies for Learning under Partial Observability
Hai Nguyen, Andrea Baisero, Dian Wang +2
Reinforcement learning in partially observable domains is challenging due to the lack of observable state information. Thankfully, learning offline in a simulator with such state i…
Hierarchical Reinforcement Learning under Mixed Observability
Hai Nguyen, Zhihan Yang, Andrea Baisero +3
The framework of mixed observable Markov decision processes (MOMDP) models many robotic domains in which some state variables are fully observable while others are not. In this wor…
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…
Reconciling Rewards with Predictive State Representations
Andrea Baisero, Christopher Amato
Predictive state representations (PSRs) are models of controlled non-Markov observation sequences which exhibit the same generative process governing POMDP observations without rel…
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…
Active Goal Recognition
Christopher Amato, Andrea Baisero
To coordinate with other systems, agents must be able to determine what the systems are currently doing and predict what they will be doing in the future---plan and goal recognitio…
Identification of Unmodeled Objects from Symbolic Descriptions
Andrea Baisero, Stefan Otte, Peter Englert +1
Successful human-robot cooperation hinges on each agent's ability to process and exchange information about the shared environment and the task at hand. Human communication is prim…
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…