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Found 9 papers, 4 papers with code
Robust Active Measuring under Model Uncertainty
Partial observability and uncertainty are common problems in sequential decision-making that particularly impede the use of formal models such as Markov decision processes (MDPs).
Factored Online Planning in Many-Agent POMDPs
However, the challenges of value estimation and belief estimation have only been tackled individually, which prevents existing methods from scaling to settings with many agents.
Reinforcement Learning by Guided Safe Exploration
Drawing from transfer learning, we also regularize a target policy (the student) towards the guide while the student is unreliable and gradually eliminate the influence of the guide as training progresses.
More for Less: Safe Policy Improvement With Stronger Performance Guarantees
In an offline reinforcement learning setting, the safe policy improvement (SPI) problem aims to improve the performance of a behavior policy according to which sample data has been generated.
Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring
In these models, actions consist of two components: a control action that affects the environment, and a measurement action that affects what the agent can observe.
Decision-Making Under Uncertainty: Beyond Probabilities
In this paper, the focus is on the uncertainty that goes beyond this classical interpretation, particularly by employing a clear distinction between aleatoric and epistemic uncertainty.
Safe Policy Improvement for POMDPs via Finite-State Controllers
In our novel approach to the SPI problem for POMDPs, we assume that a finite-state controller (FSC) represents the behavior policy and that finite memory is sufficient to derive optimal policies.
Robust Anytime Learning of Markov Decision Processes
Markov decision processes (MDPs) are formal models commonly used in sequential decision-making.
Safe Policy Improvement with an Estimated Baseline Policy
Previous work has shown the unreliability of existing algorithms in the batch Reinforcement Learning setting, and proposed the theoretically-grounded Safe Policy Improvement with Baseline Bootstrapping (SPIBB) fix: reproduce the baseline policy in the uncertain state-action pairs, in order to control the variance on the trained policy performance.
