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Found 5 papers, 1 papers with code
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
This is the first theoretical result for randomized exploration in cooperative MARL.
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment
Traditional commercial DBS devices are only able to deliver fixed-frequency periodic pulses to the basal ganglia (BG) regions of the brain, i. e., continuous DBS (cDBS).
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs
Specifically, we assume there is a local reward for each hyperedge, and the reward of the joint arm is the sum of these local rewards.
Robust Reinforcement Learning through Efficient Adversarial Herding
In this work, we extend the two-player game by introducing an adversarial herd, which involves a group of adversaries, in order to address ($\textit{i}$) the difficulty of the inner optimization problem, and ($\textit{ii}$) the potential over pessimism caused by the selection of a candidate adversary set that may include unlikely scenarios.
Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning
One of the key challenges to deep reinforcement learning (deep RL) is to ensure safety at both training and testing phases.
