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unmanned aerial vehicle path planning algorithm based on deep reinforcement learning  (IEEE Access)

 
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    IEEE Access unmanned aerial vehicle path planning algorithm based on deep reinforcement learning
    Unmanned Aerial Vehicle Path Planning Algorithm Based On Deep Reinforcement Learning, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/reinforcement+learning+algorithm/unmanned+aerial+vehicle+path+planning+algorithm+based+on+deep+reinforcement+learning/pm40667977-620-9-17
    Average 90 stars, based on 1 article reviews
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    Article Title: Adaptive Modular Reinforcement Learning for Robot Controlled in Multiple Environments
    Article Snippet: Citation information: DOI 10.1109/ACCESS.2021.3070704, IEEE Access T. Iwata et al.: Adaptive Modular Reinforcement Learning for Robot Controlled in Multiple Environments 1 Algorithm 1: Adaptive Modular Reinforcement Learning Algorithm Input: Initial modules M0 = {( μ(i), Q (i) eval, μ (i) eval )} i Initial module index to execute first action I ∈ {1, . . . , |M0|} Lower limit of evaluation value ρ Create module frequency λ Methods for creating a module createModule Methods for updating a module updateModule 1 if M0 6= ∅ then 2 M←M0 3 else 4 ( μ(1), Q (1) eval, μ (1) eval ) ← createModule() 5 M← {( μ(1), Q (1) eval, μ (1) eval )} 6 I ← 1 7 end 8 Set a set of indices for module I ← {1, . . . , |M|} 9 Initialize selected module counter ∀i ∈ I, N (i) = 0 10 Initialize x0 (Initial state) 11 u0 ← μ(I)(x0) 12 Execute action u0 and observe reward r1 and successive state x1 13 for t = 1,∞ do 14 Evaluate each module: ∀i ∈ I, δ(i)t = − ∣∣∣Q(i)eval(xt−1, ut−1)− { rt + γQ (i) eval(xt, μ (i) eval(xt)) }∣∣∣ 15 I = argmax i∈I δ (i) t 16 if δ(I)t < ρ then 17 if min i∈I N (i) > λ then 18 I ← max i∈I i+ 1 19 ( μ(I), Q (I) eval, μ (I) eval ) ← createModule(M, {δ(i)t }|I|1 ) 20 M←M∪ ( μ(I), Q (I) eval, μ (I) eval ) 21 I ← I ∪ {I} 22 N (I) ← 0 23 else 24 I ← argmin i∈I N (i) 25 end 26 end 27 Increment selected module counter N (I) ← N (I) + 1 28 ( μ(I), Q (I) eval, μ (I) eval ) ← updateModule(xt−1, ut−1, xt, rt, I) 29 ut ← μ(I)(xt) 30 Execute action ut and observe reward rt+1 and successive state xt+1 31 end a module, and the learning period of a new module are set in advance.

    Article Title: Optimizing Joint Bidding and Incentivizing Strategy for Price-Maker Load Aggregators Based on Multi-Task Multi-Agent Deep Reinforcement Learning
    Article Snippet: [16] D. Liu, W. Wang, L. Wang, H. Jia, and M. Shi, ‘‘Dynamic pricing strategy of electric vehicle aggregators based on DDPG reinforcement learning algorithm,’’ IEEE Access, vol.



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