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Found 5 papers, 0 papers with code
Bag of Policies for Distributional Deep Exploration
To test whether optimistic ensemble method can improve on distributional RL as did on scalar RL, by e. g. Bootstrapped DQN, we implement the BoP approach with a population of distributional actor-critics using Bayesian Distributional Policy Gradients (BDPG).
Bayesian Distributional Policy Gradients
Distributional Reinforcement Learning (RL) maintains the entire probability distribution of the reward-to-go, i. e. the return, providing more learning signals that account for the uncertainty associated with policy performance, which may be beneficial for trading off exploration and exploitation and policy learning in general.
Optimizing Medical Treatment for Sepsis in Intensive Care: from Reinforcement Learning to Pre-Trial Evaluation
Our aim is to establish a framework where reinforcement learning (RL) of optimizing interventions retrospectively allows us a regulatory compliant pathway to prospective clinical testing of the learned policies in a clinical deployment.
Optimizing Sequential Medical Treatments with Auto-Encoding Heuristic Search in POMDPs
Health-related data is noisy and stochastic in implying the true physiological states of patients, limiting information contained in single-moment observations for sequential clinical decision making.
The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Making
We capture this situation with partially observable Markov decision process, in which an agent optimises its actions in a belief represented as a distribution of patient states inferred from individual history trajectories.
