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Cristian Meo

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Publications

Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases
Cristian Meo
Akihiro Nakano
Mircea Tudor Lică
Aniket Rajiv Didolkar
Masahiro Suzuki
Anirudh Goyal
Mengmi Zhang
Justin Dauwels
Yutaka Matsuo
Yoshua Bengio
2024-10-09
NeurIPS.cc/2024/Workshop/Compositional_Learning (poster)
doi.org
openreview.net
Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning
Dianbo Liu
Vedant Shah
Oussama Boussif
Cristian Meo
Anirudh Goyal
Tianmin Shu
Michael Mozer
Nicolas Heess
Yoshua Bengio
In cooperative multi-agent reinforcement learning, a team of agents works together to achieve a common goal. Different environments or tasks… (see more) may require varying degrees of coordination among agents in order to achieve the goal in an optimal way. The nature of coordination will depend on the properties of the environment -- its spatial layout, distribution of obstacles, dynamics, etc. We term this variation of properties within an environment as heterogeneity. Existing literature has not sufficiently addressed the fact that different environments may have different levels of heterogeneity. We formalize the notions of coordination level and heterogeneity level of an environment and present HECOGrid, a suite of multi-agent RL environments that facilitates empirical evaluation of different MARL approaches across different levels of coordination and environmental heterogeneity by providing a quantitative control over coordination and heterogeneity levels of the environment. Further, we propose a Centralized Training Decentralized Execution learning approach called Stateful Active Facilitator (SAF) that enables agents to work efficiently in high-coordination and high-heterogeneity environments through a differentiable and shared knowledge source used during training and dynamic selection from a shared pool of policies. We evaluate SAF and compare its performance against baselines IPPO and MAPPO on HECOGrid. Our results show that SAF consistently outperforms the baselines across different tasks and different heterogeneity and coordination levels. We release the code for HECOGrid as well as all our experiments.
2023-01-31
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel
Dianbo Liu
Vedant Shah
Oussama Boussif
Cristian Meo
Anirudh Goyal
Tianmin Shu
Michael Curtis Mozer
Nicolas Heess
Yoshua Bengio
In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one a… (see more)nother. In this paper, we propose an alternative approach whereby agents communicate through an intelligent facilitator that learns to sift through and interpret signals provided by all agents to improve the agents’ collective performance. To ensure that this facilitator does not become a centralized controller, agents are incentivized to reduce their dependence on the messages it conveys, and the messages can only influence the selection of a policy from a fixed set, not instantaneous actions given the policy. We demonstrate the strength of this architecture over existing baselines on several cooperative MARL environments.
2022-05-20
ArXiv (preprint)
doi.org
arxiv.org