Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366999565> ?p ?o ?g. }
Showing items 1 to 53 of
53
with 100 items per page.
- W4366999565 abstract "Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle (AGV) navigation utilizing raw sensor data without a-priori maps or GPS, which is a necessity in hazardous, information poor environments such as regions where natural disasters occur, and extraterrestrial planets. The substantial training time required to learn an optimal DRL policy, which can be days or weeks for complex tasks, is a major hurdle to real-world implementation in AGV applications. Training entails repeated collisions with the surrounding environment over an extended time period, dependent on the complexity of the task, to reinforce positive exploratory, application specific behavior that is expensive, and time consuming in the real-world. Effectively bridging the simulation to real-world gap is a requisite for successful implementation of DRL in complex AGV applications, enabling learning of cost-effective policies. We present AutoVRL, an open-source high fidelity simulator built upon the Bullet physics engine utilizing OpenAI Gym and Stable Baselines3 in PyTorch to train AGV DRL agents for sim-to-real policy transfer. AutoVRL is equipped with sensor implementations of GPS, IMU, LiDAR and camera, actuators for AGV control, and realistic environments, with extensibility for new environments and AGV models. The simulator provides access to state-of-the-art DRL algorithms, utilizing a python interface for simple algorithm and environment customization, and simulation execution." @default.
- W4366999565 created "2023-04-27" @default.
- W4366999565 creator A5014199018 @default.
- W4366999565 creator A5024215697 @default.
- W4366999565 creator A5034350392 @default.
- W4366999565 date "2023-04-22" @default.
- W4366999565 modified "2023-09-26" @default.
- W4366999565 title "AutoVRL: A High Fidelity Autonomous Ground Vehicle Simulator for Sim-to-Real Deep Reinforcement Learning" @default.
- W4366999565 doi "https://doi.org/10.48550/arxiv.2304.11496" @default.
- W4366999565 hasPublicationYear "2023" @default.
- W4366999565 type Work @default.
- W4366999565 citedByCount "0" @default.
- W4366999565 crossrefType "posted-content" @default.
- W4366999565 hasAuthorship W4366999565A5014199018 @default.
- W4366999565 hasAuthorship W4366999565A5024215697 @default.
- W4366999565 hasAuthorship W4366999565A5034350392 @default.
- W4366999565 hasBestOaLocation W43669995651 @default.
- W4366999565 hasConcept C111919701 @default.
- W4366999565 hasConcept C154945302 @default.
- W4366999565 hasConcept C2776459999 @default.
- W4366999565 hasConcept C41008148 @default.
- W4366999565 hasConcept C44154836 @default.
- W4366999565 hasConcept C519991488 @default.
- W4366999565 hasConcept C60229501 @default.
- W4366999565 hasConcept C76155785 @default.
- W4366999565 hasConcept C79403827 @default.
- W4366999565 hasConcept C97541855 @default.
- W4366999565 hasConceptScore W4366999565C111919701 @default.
- W4366999565 hasConceptScore W4366999565C154945302 @default.
- W4366999565 hasConceptScore W4366999565C2776459999 @default.
- W4366999565 hasConceptScore W4366999565C41008148 @default.
- W4366999565 hasConceptScore W4366999565C44154836 @default.
- W4366999565 hasConceptScore W4366999565C519991488 @default.
- W4366999565 hasConceptScore W4366999565C60229501 @default.
- W4366999565 hasConceptScore W4366999565C76155785 @default.
- W4366999565 hasConceptScore W4366999565C79403827 @default.
- W4366999565 hasConceptScore W4366999565C97541855 @default.
- W4366999565 hasLocation W43669995651 @default.
- W4366999565 hasOpenAccess W4366999565 @default.
- W4366999565 hasPrimaryLocation W43669995651 @default.
- W4366999565 hasRelatedWork W2327204559 @default.
- W4366999565 hasRelatedWork W260766989 @default.
- W4366999565 hasRelatedWork W2959276766 @default.
- W4366999565 hasRelatedWork W3074294383 @default.
- W4366999565 hasRelatedWork W3111983280 @default.
- W4366999565 hasRelatedWork W3129254793 @default.
- W4366999565 hasRelatedWork W3139193008 @default.
- W4366999565 hasRelatedWork W3164468573 @default.
- W4366999565 hasRelatedWork W4206669594 @default.
- W4366999565 hasRelatedWork W4295941380 @default.
- W4366999565 isParatext "false" @default.
- W4366999565 isRetracted "false" @default.
- W4366999565 workType "article" @default.