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Found 6 papers, 1 papers with code
Imitating Human Behaviour with Diffusion Models
This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments.
Fully Distributed Actor-Critic Architecture for Multitask Deep Reinforcement Learning
We propose a fully distributed actor-critic architecture, named Diff-DAC, with application to multitask reinforcement learning (MRL).
Compatible features for Monotonic Policy Improvement
In this work we establish conditions under which the parametric approximation of the critic does not introduce bias to the updates of surrogate objective.
Learning Parametric Closed-Loop Policies for Markov Potential Games
This is a considerable improvement over the previously standard approach for the CL analysis of MPGs, which gives no approximate solution if no NE belongs to the chosen parametric family, and which is practical only for simple parametric forms.
Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning
We propose a fully distributed actor-critic algorithm approximated by deep neural networks, named \textit{Diff-DAC}, with application to single-task and to average multitask reinforcement learning (MRL).
Distributed Policy Evaluation Under Multiple Behavior Strategies
We apply diffusion strategies to develop a fully-distributed cooperative reinforcement learning algorithm in which agents in a network communicate only with their immediate neighbors to improve predictions about their environment.
