1 citations · 2 across the 8 of their papers we have counts for
7 papers · 1 filter
Learning Mixture Density via Natural Gradient Expectation Maximization
Yutao Chen, Jasmine Bayrooti, Steven Morad
Mixture density networks are neural networks that produce Gaussian mixtures to represent continuous multimodal conditional densities. Standard training procedures involve maximum l…
Investigating Memory in Model-Free RL with POPGym Arcade
Zekang Wang, Zhe He, Borong Zhang +2
How should we analyze memory in deep RL? We introduce tools for analyzing policies under partial observability and revealing how agents use memory to make decisions. To utilize the…
Recurrent Reinforcement Learning with Memoroids
Steven Morad, Chris Lu, Ryan Kortvelesy +3
Memory models such as Recurrent Neural Networks (RNNs) and Transformers address Partially Observable Markov Decision Processes (POMDPs) by mapping trajectories to latent Markov sta…
Reinforcement Learning with Fast and Forgetful Memory
Steven Morad, Ryan Kortvelesy, Stephan Liwicki +1
Nearly all real world tasks are inherently partially observable, necessitating the use of memory in Reinforcement Learning (RL). Most model-free approaches summarize the trajectory…
Generalised f-Mean Aggregation for Graph Neural Networks
Ryan Kortvelesy, Steven Morad, Amanda Prorok
Graph Neural Network (GNN) architectures are defined by their implementations of update and aggregation modules. While many works focus on new ways to parametrise the update module…
POPGym: Benchmarking Partially Observable Reinforcement Learning
Steven Morad, Ryan Kortvelesy, Matteo Bettini +2
Real world applications of Reinforcement Learning (RL) are often partially observable, thus requiring memory. Despite this, partial observability is still largely ignored by contem…