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Found 7 papers, 2 papers with code
NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios
Offline reinforcement learning (RL) aims to learn from historical data without requiring (costly) access to the environment.
Scaling Multi-Objective Security Games Provably via Space Discretization Based Evolutionary Search
Then, a many-objective EA is used for optimization in the low-dimensional discrete solution space to obtain a well-spaced Pareto front.
Unified Policy Optimization for Continuous-action Reinforcement Learning in Non-stationary Tasks and Games
This paper addresses policy learning in non-stationary environments and games with continuous actions.
Transferable Reward Learning by Dynamics-Agnostic Discriminator Ensemble
In this work, we present a dynamics-agnostic discriminator-ensemble reward learning method (DARL) within the AIL framework, capable of learning both state-action and state-only reward functions.
Improving Fictitious Play Reinforcement Learning with Expanding Models
However, learning to beat a pool in stochastic games, i. e., a wide distribution over policy models, is either sample-consuming or insufficient to exploit all models with limited amount of samples.
Multi-View Large-Scale Bundle Adjustment Method for High-Resolution Satellite Images
Given enough multi-view image corresponding points (also called tie points) and ground control points (GCP), bundle adjustment for high-resolution satellite images is used to refine the orientations or most often used geometric parameters Rational Polynomial Coefficients (RPC) of each satellite image in a unified geodetic framework, which is very critical in many photogrammetry and computer vision applications.
Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model
Nowadays dense stereo matching has become one of the dominant tools in 3D reconstruction of urban regions for its low cost and high flexibility in generating dense 3D points.
