Paper Reading AI Learner

Pathfinding in Random Partially Observable Environments with Vision-Informed Deep Reinforcement Learning

2022-09-11 06:32:00
Anthony Dowling

Abstract

Deep reinforcement learning is a technique for solving problems in a variety of environments, ranging from Atari video games to stock trading. This method leverages deep neural network models to make decisions based on observations of a given environment with the goal of maximizing a reward function that can incorporate cost and rewards for reaching goals. With the aim of pathfinding, reward conditions can include reaching a specified target area along with costs for movement. In this work, multiple Deep Q-Network (DQN) agents are trained to operate in a partially observable environment with the goal of reaching a target zone in minimal travel time. The agent operates based on a visual representation of its surroundings, and thus has a restricted capability to observe the environment. A comparison between DQN, DQN-GRU, and DQN-LSTM is performed to examine each models capabilities with two different types of input. Through this evaluation, it is been shown that with equivalent training and analogous model architectures, a DQN model is able to outperform its recurrent counterparts.

Abstract (translated)

URL

https://arxiv.org/abs/2209.04801

PDF

https://arxiv.org/pdf/2209.04801.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot