Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management Problem
Fuente:
arXiv
Saved in:
| Main Authors: | Pendyala, Abhijeet, Glasmachers, Tobias |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
by: Pendyala, Abhijeet, et al.
Published: (2024)
by: Pendyala, Abhijeet, et al.
Published: (2024)
Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environments
by: Maus, Tom, et al.
Published: (2025)
by: Maus, Tom, et al.
Published: (2025)
SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process
by: Maus, Tom, et al.
Published: (2025)
by: Maus, Tom, et al.
Published: (2025)
Deep Reinforcement Learning Based Navigation with Macro Actions and Topological Maps
by: Hakenes, Simon, et al.
Published: (2025)
by: Hakenes, Simon, et al.
Published: (2025)
Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control
by: Maus, Tom, et al.
Published: (2026)
by: Maus, Tom, et al.
Published: (2026)
Pulsar Detection with Deep Learning
by: Pendyala, Manideep
Published: (2025)
by: Pendyala, Manideep
Published: (2025)
Cumulative Learning Rate Adaptation: Revisiting Path-Based Schedules for SGD and Adam
by: Atamna, Asma, et al.
Published: (2025)
by: Atamna, Asma, et al.
Published: (2025)
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
by: Vargas, Francisco, et al.
Published: (2023)
by: Vargas, Francisco, et al.
Published: (2023)
Balancing Specialization and Centralization: A Multi-Agent Reinforcement Learning Benchmark for Sequential Industrial Control
by: Maus, Tom, et al.
Published: (2025)
by: Maus, Tom, et al.
Published: (2025)
Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation
by: Sziburis, Tim, et al.
Published: (2024)
by: Sziburis, Tim, et al.
Published: (2024)
Compositional Monte Carlo Tree Diffusion for Extendable Planning
by: Yoon, Jaesik, et al.
Published: (2025)
by: Yoon, Jaesik, et al.
Published: (2025)
ProtoP-OD: Explainable Object Detection with Prototypical Parts
by: Rath-Manakidis, Pavlos, et al.
Published: (2024)
by: Rath-Manakidis, Pavlos, et al.
Published: (2024)
MatRL: Provably Generalizable Iterative Algorithm Discovery via Monte-Carlo Tree Search
by: Kim, Sungyoon, et al.
Published: (2025)
by: Kim, Sungyoon, et al.
Published: (2025)
Sampling conditioned diffusions via Pathspace Projected Monte Carlo
by: Grafke, Tobias
Published: (2025)
by: Grafke, Tobias
Published: (2025)
Monte Carlo Stochastic Depth for Uncertainty Estimation in Deep Learning
by: Müller, Adam T., et al.
Published: (2026)
by: Müller, Adam T., et al.
Published: (2026)
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL
by: Wagenmaker, Andrew, et al.
Published: (2024)
by: Wagenmaker, Andrew, et al.
Published: (2024)
Variable Metric Evolution Strategies for High-dimensional Multi-Objective Optimization
by: Glasmachers, Tobias
Published: (2024)
by: Glasmachers, Tobias
Published: (2024)
Monte Carlo Tree Diffusion for System 2 Planning
by: Yoon, Jaesik, et al.
Published: (2025)
by: Yoon, Jaesik, et al.
Published: (2025)
A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning
by: Engel, Jens, et al.
Published: (2025)
by: Engel, Jens, et al.
Published: (2025)
Inference Time Policy Optimization for Offline RL with Differentiable World Models
by: Deb, Rohan, et al.
Published: (2026)
by: Deb, Rohan, et al.
Published: (2026)
C-MCTS: Safe Planning with Monte Carlo Tree Search
by: Parthasarathy, Dinesh, et al.
Published: (2023)
by: Parthasarathy, Dinesh, et al.
Published: (2023)
Fast and Robust Simulation-Based Inference With Optimization Monte Carlo
by: Gkolemis, Vasilis, et al.
Published: (2025)
by: Gkolemis, Vasilis, et al.
Published: (2025)
Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo
by: Feng, Shengyu, et al.
Published: (2024)
by: Feng, Shengyu, et al.
Published: (2024)
Plan2Cleanse: Test-Time Backdoor Defense via Monte-Carlo Planning in Deep Reinforcement Learning
by: Chen, Sze-Ann, et al.
Published: (2026)
by: Chen, Sze-Ann, et al.
Published: (2026)
Online Min-Max Optimization: From Individual Regrets to Cumulative Saddle Points
by: Vyas, Abhijeet, et al.
Published: (2026)
by: Vyas, Abhijeet, et al.
Published: (2026)
Optimizing Prompt Sequences using Monte Carlo Tree Search for LLM-Based Optimization
by: Yu, Fei Xu, et al.
Published: (2025)
by: Yu, Fei Xu, et al.
Published: (2025)
Sequential Monte Carlo for Policy Optimization in Continuous POMDPs
by: Abdulsamad, Hany, et al.
Published: (2025)
by: Abdulsamad, Hany, et al.
Published: (2025)
RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning
by: Lei, Kun, et al.
Published: (2025)
by: Lei, Kun, et al.
Published: (2025)
VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learning
by: de Vries, Joery A., et al.
Published: (2026)
by: de Vries, Joery A., et al.
Published: (2026)
SPEED-RL: Faster Training of Reasoning Models via Online Curriculum Learning
by: Zhang, Ruiqi, et al.
Published: (2025)
by: Zhang, Ruiqi, et al.
Published: (2025)
Tempered Sequential Monte Carlo for Trajectory and Policy Optimization with Differentiable Dynamics
by: Yang, Heng
Published: (2026)
by: Yang, Heng
Published: (2026)
The Era of Real-World Human Interaction: RL from User Conversations
by: Jin, Chuanyang, et al.
Published: (2025)
by: Jin, Chuanyang, et al.
Published: (2025)
Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents
by: Han, Shuo, et al.
Published: (2025)
by: Han, Shuo, et al.
Published: (2025)
Subspace Langevin Monte Carlo
by: Maunu, Tyler, et al.
Published: (2024)
by: Maunu, Tyler, et al.
Published: (2024)
Confounding is a Pervasive Problem in Real World Recommender Systems
by: Merkov, Alexander, et al.
Published: (2025)
by: Merkov, Alexander, et al.
Published: (2025)
Object-Centric World Models Meet Monte Carlo Tree Search
by: Vakhitov, Rodion, et al.
Published: (2026)
by: Vakhitov, Rodion, et al.
Published: (2026)
Plan Before You Trade: Inference-Time Optimization for RL Trading Agents
by: Go, Eun, et al.
Published: (2026)
by: Go, Eun, et al.
Published: (2026)
The Implicit Curriculum: Learning Dynamics in RL with Verifiable Rewards
by: Huang, Yu, et al.
Published: (2026)
by: Huang, Yu, et al.
Published: (2026)
Monte Carlo and quasi-Monte Carlo integration for likelihood functions
by: Tang, Yanbo
Published: (2025)
by: Tang, Yanbo
Published: (2025)
Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
by: Kelvinius, Filip Ekström, et al.
Published: (2025)
by: Kelvinius, Filip Ekström, et al.
Published: (2025)
Similar Items
-
Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
by: Pendyala, Abhijeet, et al.
Published: (2024) -
Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environments
by: Maus, Tom, et al.
Published: (2025) -
SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process
by: Maus, Tom, et al.
Published: (2025) -
Deep Reinforcement Learning Based Navigation with Macro Actions and Topological Maps
by: Hakenes, Simon, et al.
Published: (2025) -
Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control
by: Maus, Tom, et al.
Published: (2026)