Natural actor-critic for robust reinforcement learning with function approximation
We study robust reinforcement learning (RL) with the goal of determining a well-performing
policy that is robust against model mismatch between the training simulator and the testing …
policy that is robust against model mismatch between the training simulator and the testing …
Double pessimism is provably efficient for distributionally robust offline reinforcement learning: Generic algorithm and robust partial coverage
We study distributionally robust offline reinforcement learning (RL), which seeks to find an
optimal robust policy purely from an offline dataset that can perform well in perturbed …
optimal robust policy purely from an offline dataset that can perform well in perturbed …
Learning cut selection for mixed-integer linear programming via hierarchical sequence model
Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which
formulate a wide range of important real-world applications. Cut selection--which aims to …
formulate a wide range of important real-world applications. Cut selection--which aims to …
Adjustable robust reinforcement learning for online 3d bin packing
Designing effective policies for the online 3D bin packing problem (3D-BPP) has been a
long-standing challenge, primarily due to the unpredictable nature of incoming box …
long-standing challenge, primarily due to the unpredictable nature of incoming box …
Learning to stop cut generation for efficient mixed-integer linear programming
Cutting planes (cuts) play an important role in solving mixed-integer linear programs
(MILPs), as they significantly tighten the dual bounds and improve the solving performance …
(MILPs), as they significantly tighten the dual bounds and improve the solving performance …
UAV air combat autonomous trajectory planning method based on robust adversarial reinforcement learning
L Wang, S Zheng, S Tai, H Liu, T Yue - Aerospace Science and Technology, 2024 - Elsevier
The poor robustness of the air combat autonomous trajectory planning strategy (ATP)
trained through vanilla reinforcement learning (RL) methods is attributed to its dependence …
trained through vanilla reinforcement learning (RL) methods is attributed to its dependence …
Provable sim-to-real transfer in continuous domain with partial observations
Sim-to-real transfer trains RL agents in the simulated environments and then deploys them
in the real world. Sim-to-real transfer has been widely used in practice because it is often …
in the real world. Sim-to-real transfer has been widely used in practice because it is often …
Minimax optimal and computationally efficient algorithms for distributionally robust offline reinforcement learning
Distributionally robust offline reinforcement learning (RL), which seeks robust policy training
against environment perturbation by modeling dynamics uncertainty, calls for function …
against environment perturbation by modeling dynamics uncertainty, calls for function …
Optimal transport perturbations for safe reinforcement learning with robustness guarantees
Robustness and safety are critical for the trustworthy deployment of deep reinforcement
learning in real-world decision making applications. In particular, we require algorithms that …
learning in real-world decision making applications. In particular, we require algorithms that …
[HTML][HTML] Tube-based robust reinforcement learning for autonomous maneuver decision for UCAVs
W Lixin, S ZHENG, P Haiyin, LU Changqian… - Chinese Journal of …, 2024 - Elsevier
Reinforcement Learning (RL) algorithms enhance intelligence of air combat Autonomous
Maneuver Decision (AMD) policy, but they may underperform in target combat environments …
Maneuver Decision (AMD) policy, but they may underperform in target combat environments …