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+data/2023_05_10__16_59/Distributed_and_Distribution_Robust_Meta_Reinforcement_Learning____D2_RMRL__for_Data_Pre_storing_and_Routing_in_Cube_Satellite_Networks.pdf filter=lfs diff=lfs merge=lfs -text
+data/2023_05_10__16_59/Exposure_Based_Multi_Agent_Inspection_of_a_Tumbling_Target_Using_Deep___Reinforcement_Learning.pdf filter=lfs diff=lfs merge=lfs -text
+data/2023_05_10__16_59/Mastering_Spatial_Graph_Prediction_of_Road_Networks.pdf filter=lfs diff=lfs merge=lfs -text
+data/2023_05_10__16_59/Mission_schedule_of_agile_satellites_based_on_Proximal_Policy___Optimization_Algorithm.pdf filter=lfs diff=lfs merge=lfs -text
+data/2023_05_10__16_59/Quantum_algorithms_applied_to_satellite_mission_planning_for_Earth___observation.pdf filter=lfs diff=lfs merge=lfs -text
+data/2023_05_10__16_59/RL_GA__A_Reinforcement_Learning_Based_Genetic_Algorithm_for___Electromagnetic_Detection_Satellite_Scheduling_Problem.pdf filter=lfs diff=lfs merge=lfs -text
+data/2023_05_10__16_59/Towards_Resilient_Access_Equality_for_6G_Serverless_p_LEO_Satellite___Networks.pdf filter=lfs diff=lfs merge=lfs -text

data/1111.txt

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+Question: What are the application scenarios of reinforcement learning in the space satellite field?
+Answer: 
+Reinforcement learning can be applied to the space satellite field to optimize fuel consumption, design low-thrust trajectories, and autonomously control satellites during the cruise. It can also be used to explore trajectories in environments where a reference solution is not known, as well as to solve multi-satellite problems.
+
+Question: The technical route of reinforcement learning in optimizing fuel consumption
+Answer: for satellite trajectory optimization is promising because 
+
+it does not require a deep understanding of mathematics or space dynamics, it can be implemented using open-source tools and no expensive computational resources, and it can autonomously learn the best approach to achieve the desired result while minimizing fuel consumption. Additionally, it can be used to explore new strategies in other areas where solutions have room for improvement, such as collision avoidance, geostationary satellite co-location or multi-satellite formation.
+
+Question: What kind of questions that can be answered?
+Answer: 
+Questions that can be answered with the given context information include: 
+1. What is the goal of Visual Active Search (VAS)? 
+2. What is the proposed deep reinforcement learning approach for VAS? 
+3. What are the two variants of test-time adaptation (TTA) proposed for VAS? 
+4. What are the results of the experiments performed on two publicly available satellite imagery datasets? 
+5. What is the optimization problem that needs to be solved in order to succeed in VAS? 
+6. How does the greedy policy fail to capture important problem structure in VAS?
+
+Question: What is the goal of Visual Active Search (VAS)?
+Answer: 
+The goal of Visual Active Search (VAS) is to identify as many examples of a target object as possible from a broad area image, using a limited sequence of queries. Each query is informative about the spatial distribution of target objects beyond what is captured visually, such as due to spatial correlation.
+
+Question: ǿ��ѧϰ�ڹ���Ż��ϵ�Ӧ�ò���
+Answer: 
+The steps for applying reinforcement learning to track optimization include: 1) Defining the environment and the reward system; 2) Establishing a policy for the agent to follow; 3) Training the agent using trial and error; 4) Evaluating the performance of the agent; and 5) Refining the policy and reward system as needed.
+
+Question: ǿ��ѧϰ����Ƶ������켣�ϵIJ���
+Answer: 
+Steps for using reinforcement learning to design low-inference trajectories include: 1) defining the environment and the reward system; 2) training an agent to learn the optimal policy; 3) testing the agent in the environment; 4) evaluating the performance of the agent; and 5) refining the agent's policy based on the evaluation results.
+
+Question: ǿ��ѧϰ��ζ������Ż�����Ļ�����
+Answer: 
+Reinforcement Learning defines the environment of an orbital optimization problem as a communication between an agent and an environment. The environment provides the initial observation to the agent, and in each step, the agent decides the best action to take and informs the environment about it. The environment then rewards or penalizes the latest action and returns the new observation to the agent. This process is repeated until the environment considers that the scenario is over.
+
+Question: ǿ��ѧϰ����ƶ�����Ż�����Ľ����ƶȣ�
+Answer: 
+Reinforcement learning uses rewards to incentivize the optimization of track problems. Rewards are determined by the difference in the value of the objective function between a step and its previous step. Rewards are also determined by the state of the model, the expected long term profit of each action, and the value function which is estimated using artificial neural networks. Additionally, reinforcement learning uses two strategies, "Exploitation" and "Exploration", to choose actions. "Exploitation" uses the current best action, while "Exploration" uses a random action to explore new possibilities.
+

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