latentmas

We have hosted the application latentmas in order to run this application in our online workstations with Wine or directly.

Quick description about latentmas:

LatentMAS is an advanced framework for multi-agent reinforcement learning (MARL) that uses latent variable modeling to bridge perception and decision-making in environments where agents must coordinate under uncertainty. It provides mechanisms for agents to learn high-level latent representations of states, which simplifies complex sensory inputs into compact, actionable embeddings that facilitate both individual policy learning and inter-agent coordination. Using this latent space, the framework enables Multi-Agent Systems (MAS) to scale more effectively in environments with high dimensionality — such as robotics, simulated physics tasks, and strategic games — by reducing redundant learning burdens and focusing agent exploration. LatentMAS also implements centralized training with decentralized execution, letting agents share learned representations during training while operating autonomously at inference time.

Features:

• Latent representation learning for multi-agent systems
• Centralized training with decentralized execution architecture
• Benchmarking environments and evaluation tools
• Scalable coordination under uncertainty
• Policy optimization built for high-dimensional inputs
• Example implementations for robotics and simulated tasks

Programming Language: Python.
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