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Found 7 papers, 1 papers with code
Minimax-Bayes Reinforcement Learning
While the Bayesian decision-theoretic framework offers an elegant solution to the problem of decision making under uncertainty, one question is how to appropriately select the prior distribution.
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
Then, we propose a generic two-stage algorithm, MLEMTRL, to address the MTRL problem in discrete and continuous settings.
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty
In existing literature, the risk in stochastic games has been studied in terms of the inherent uncertainty evoked by the variability of transitions and actions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling
We study the problem of performing automated experiment design for drug screening through Bayesian inference and optimisation.
SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning
In this paper, we consider risk-sensitive sequential decision-making in Reinforcement Learning (RL).
Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning
Bayesian reinforcement learning (BRL) offers a decision-theoretic solution for reinforcement learning.
Epistemic Risk-Sensitive Reinforcement Learning
The risk-averse behavior is then compared with the behavior of the optimal risk-neutral policy in environments with epistemic risk.
