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Found 13 papers, 3 papers with code
Model-based Offline Quantum Reinforcement Learning
The policy is optimized with a gradient-free optimization scheme using the return estimate given by the model as the fitness function.
Learning Control Policies for Variable Objectives from Offline Data
Offline reinforcement learning provides a viable approach to obtain advanced control strategies for dynamical systems, in particular when direct interaction with the environment is not available.
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 reinforcement learning method.
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning
Offline reinforcement learning (RL) Algorithms are often designed with environments such as MuJoCo in mind, in which the planning horizon is extremely long and no noise exists.
Trustworthy AI for Process Automation on a Chylla-Haase Polymerization Reactor
In this paper, genetic programming reinforcement learning (GPRL) is utilized to generate human-interpretable control policies for a Chylla-Haase polymerization reactor.
Behavior Constraining in Weight Space for Offline Reinforcement Learning
In offline reinforcement learning, a policy needs to be learned from a single pre-collected dataset.
Interpretable Control by Reinforcement Learning
In this paper, three recently introduced reinforcement learning (RL) methods are used to generate human-interpretable policies for the cart-pole balancing benchmark.
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming
Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications.
Interpretable Policies for Reinforcement Learning by Genetic Programming
Here we introduce the genetic programming for reinforcement learning (GPRL) approach based on model-based batch reinforcement learning and genetic programming, which autonomously learns policy equations from pre-existing default state-action trajectory samples.
A Benchmark Environment Motivated by Industrial Control Problems
On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand.
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks in an off-policy, batch-based setting.
Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies
To the best of our knowledge, this approach is the first to relate self-organizing fuzzy controllers to model-based batch RL.
Introduction to the "Industrial Benchmark"
A novel reinforcement learning benchmark, called Industrial Benchmark, is introduced.
