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Found 14 papers, 3 papers with code
Which Experiences Are Influential for Your Agent? Policy Iteration with Turn-over Dropout
In reinforcement learning (RL) with experience replay, experiences stored in a replay buffer influence the RL agent's performance.
Soft Sensors and Process Control using AI and Dynamic Simulation
During the operation of a chemical plant, product quality must be consistently maintained, and the production of off-specification products should be minimized.
Railway Operation Rescheduling System via Dynamic Simulation and Reinforcement Learning
The system leverages reinforcement learning and a dynamic simulator that can simulate the railway traffic and passenger flow of a whole line.
Dropout Q-Functions for Doubly Efficient Reinforcement Learning
To make REDQ more computationally efficient, we propose a method of improving computational efficiency called DroQ, which is a variant of REDQ that uses a small ensemble of dropout Q-functions.
Meta-Model-Based Meta-Policy Optimization
Model-based meta-reinforcement learning (RL) methods have recently been shown to be a promising approach to improving the sample efficiency of RL in multi-task settings.
Learning Robust Options by Conditional Value at Risk Optimization
While there are several methods to learn options that are robust against the uncertainty of model parameters, these methods only consider either the worst case or the average (ordinary) case for learning options.
Synthesizing Chemical Plant Operation Procedures using Knowledge, Dynamic Simulation and Deep Reinforcement Learning
Chemical plants are complex and dynamical systems consisting of many components for manipulation and sensing, whose state transitions depend on various factors such as time, disturbance, and operation procedures.
Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients
In this paper, we propose a framework that can automatically refine symbol grounding functions and a high-level planner to reduce human effort for designing these modules.
Monte Carlo Tree Search with Scalable Simulation Periods for Continuously Running Tasks
The evaluation results show that, with variable simulation times, the proposed approach outperforms the conventional MCTS in the evaluated continuous decision space tasks and improves the performance of MCTS in most of the ALE tasks.
Hierarchical Reinforcement Learning with Abductive Planning
One potential solution to this problem is to combine reinforcement learning with automated symbol planning and utilize prior knowledge on the domain.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Translating MFM into FOL: towards plant operation planning
This paper proposes a method to translate multilevel flow modeling (MFM) into a first-order language (FOL), which enables the utilisation of logical techniques, such as inference engines and abductive reasoners.
