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Found 7 papers, 0 papers with code
EnCoMP: Enhanced Covert Maneuver Planning using Offline Reinforcement Learning
Cover navigation in complex environments is a critical challenge for autonomous robots, requiring the identification and utilization of environmental cover while maintaining efficient navigation.
TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments
Additionally, TopoNav incorporates intrinsic motivation to guide exploration toward relevant regions and frontier nodes in the topological map, addressing the challenges of sparse extrinsic rewards.
Enhancing Robotic Navigation: An Evaluation of Single and Multi-Objective Reinforcement Learning Strategies
This study presents a comparative analysis between single-objective and multi-objective reinforcement learning methods for training a robot to navigate effectively to an end goal while efficiently avoiding obstacles.
CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning
We evaluate CoverNav using the Unity simulation environment and show that it guarantees dynamically feasible velocities in the terrain when fed with an elevation map generated by another DRL based navigation algorithm.
Follow the Soldiers with Optimized Single-Shot Multibox Detection and Reinforcement Learning
Nowadays, autonomous cars are gaining traction due to their numerous potential applications on battlefields and in resolving a variety of other real-world challenges.
Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation
Nowadays, autonomous vehicles are gaining traction due to their numerous potential applications in resolving a variety of other real-world challenges.
Ranking the locations and predicting future crime occurrence by retrieving news from different Bangla online newspapers
There have thousands of crimes are happening daily all around.
