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08/11/2023
Learning Control Policies for Variable Objectives from Offline Data
Offline reinforcement learning provides a viable approach to obtain adva...
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06/16/2023
Automatic Trade-off Adaptation in Offline RL
Recently, offline RL algorithms have been proposed that remain adaptive ...
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08/01/2022
Safe Policy Improvement Approaches and their Limitations
Safe Policy Improvement (SPI) is an important technique for offline rein...
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06/09/2022
Quantum Policy Iteration via Amplitude Estimation and Grover Search – Towards Quantum Advantage for Reinforcement Learning
We present a full implementation and simulation of a novel quantum reinf...
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05/21/2022
User-Interactive Offline Reinforcement Learning
Offline reinforcement learning algorithms still lack trust in practice d...
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01/28/2022
Safe Policy Improvement Approaches on Discrete Markov Decision Processes
Safe Policy Improvement (SPI) aims at provable guarantees that a learned...
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01/14/2022
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning
Offline reinforcement learning (RL) Algorithms are often designed with e...
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11/26/2021
Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning
Recently developed offline reinforcement learning algorithms have made i...
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07/12/2021
Behavior Constraining in Weight Space for Offline Reinforcement Learning
In offline reinforcement learning, a policy needs to be learned from a s...
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08/12/2020
Overcoming Model Bias for Robust Offline Deep Reinforcement Learning
State-of-the-art reinforcement learning algorithms mostly rely on being ...
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04/29/2018
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming
Autonomously training interpretable control strategies, called policies,...
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12/12/2017
Interpretable Policies for Reinforcement Learning by Genetic Programming
The search for interpretable reinforcement learning policies is of high ...
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12/10/2017
Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks
We derive a novel sensitivity analysis of input variables for predictive...
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10/19/2017
Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems
Bayesian neural networks (BNNs) with latent variables are probabilistic ...
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09/27/2017
A Benchmark Environment Motivated by Industrial Control Problems
In the research area of reinforcement learning (RL), frequently novel an...
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06/26/2017
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables
Bayesian neural networks (BNNs) with latent variables are probabilistic ...
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05/20/2017
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
The Particle Swarm Optimization Policy (PSO-P) has been recently introdu...
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10/19/2016
Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies
Fuzzy controllers are efficient and interpretable system controllers for...
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05/23/2016
