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Found 9 papers, 8 papers with code
Revisiting Recurrent Reinforcement Learning with Memory Monoids
Memory models such as Recurrent Neural Networks (RNNs) and Transformers address Partially Observable Markov Decision Processes (POMDPs) by mapping trajectories to latent Markov states.
Fixed Integral Neural Networks
In this work, we present a method for representing the analytical integral of a learned function $f$.
POPGym: Benchmarking Partially Observable Reinforcement Learning
Real world applications of Reinforcement Learning (RL) are often partially observable, thus requiring memory.
Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning
The problem of permutation-invariant learning over set representations is particularly relevant in the field of multi-agent systems -- a few potential applications include unsupervised training of aggregation functions in graph neural networks (GNNs), neural cellular automata on graphs, and prediction of scenes with multiple objects.
VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning
VMAS's scenarios prove challenging in orthogonal ways for state-of-the-art MARL algorithms.
QGNN: Value Function Factorisation with Graph Neural Networks
In multi-agent reinforcement learning, the use of a global objective is a powerful tool for incentivising cooperation.
The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts
Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances.
Graph Convolutional Memory using Topological Priors
Solving partially-observable Markov decision processes (POMDPs) is critical when applying reinforcement learning to real-world problems, where agents have an incomplete view of the world.
ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture
Recent work in the multi-agent domain has shown the promise of Graph Neural Networks (GNNs) to learn complex coordination strategies.
