基于生物启发式算法的多智能体强化学习算法matlab和python实现_基于生物启发式算法的多智能体强化学习算法matlab和python实现资源-CSDN文库

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m：22个

sample：13个

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matlab

强化学习

python

126 浏览量 2022-12-11 22:54:30 上传评论 3 收藏 121.21MB ZIP 举报

强化学习是一种机器学习方法，它通过与环境的交互来学习最优策略，以最大化长期奖励。在多智能体系统中，每个智能体不仅需要优化自身的奖励，还需要考虑其他智能体的行为和系统的整体效果。生物启发式算法，如遗传算法、粒子群优化等，常被用于强化学习中以加速收敛和提高解决方案的质量。本项目着重介绍了如何在MATLAB和Python环境中实现基于生物启发式算法的多智能体强化学习算法。MATLAB以其强大的数学计算能力和图形用户界面而被广泛应用于科学计算和工程领域，而Python则因为其简洁的语法和丰富的库支持成为了数据科学和机器学习的首选语言。在MATLAB实现中，我们可以利用MATLAB的RL Toolbox来构建强化学习环境和智能体模型。我们需要定义状态空间、动作空间和奖励函数，这通常涉及对问题领域的深入理解。然后，我们可以应用生物启发式算法，如遗传算法，来优化智能体的策略网络参数。遗传算法通过模拟自然选择和遗传的过程来搜索最佳解，包括选择、交叉和变异操作。在Python实现部分，可以使用开源库如gym和PyTorch来构建和训练强化学习模型。gym库提供了各种环境供学习者进行实验，而PyTorch则提供了一个灵活的深度学习框架，可用于构建和优化神经网络。同样，我们可以将生物启发式算法如粒子群优化（PSO）应用于策略优化，PSO通过模拟鸟群寻找食物的行为来全局搜索最优解。在多智能体系统中，强化学习的挑战在于协调各个智能体的行为，避免竞争和冲突，以及处理不确定性和非平稳性。为此，可以采用合作或竞争策略，例如通过中央控制器协调智能体或使用分散式通信机制。此外，生物启发式算法能帮助探索更广阔的策略空间，增强智能体的适应性和鲁棒性。这个项目结合了生物启发式算法和强化学习，旨在解决多智能体系统中的复杂决策问题。MATLAB和Python的实现提供了两种不同的编程环境和工具，使得研究者和开发者可以根据自身需求和偏好选择合适的实现方式。通过这样的结合，我们不仅可以利用强化学习的自学习能力，还能借助生物启发式算法的强大搜索能力，以实现更高效、更稳健的多智能体系统解决方案。

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基于生物启发式算法的多智能体强化学习算法matlab和python实现（328个子文件）

config 320B

description 73B

exclude 240B

HEAD 194B

HEAD 32B

HEAD 23B

pack-972df2f84bfd5b1ccafa81e758d23b2b8777dbce.idx 10KB

index 37KB

BCD_convergence.jpg 27KB

applyhatch_plusC.m 8KB

cost_d.m 6KB

cost_K.m 5KB

beta.m 5KB

Adaptive_K.m 3KB

convex.m 3KB

huatu.m 3KB

time_K.m 3KB

makehatch_plus.m 3KB

convergence.m 2KB

Partial_offloading_BCD_beta.m 2KB

Partial_offloading_BCD_beta_add.m 2KB

Partial_offloading_BCD_distance.m 2KB

Partial_offloading_BCD.m 2KB

MyLinear.m 1KB

Partial_offloading_RandomAlpha_beta.m 924B

bisection_partial_y.m 901B

Partial_offloading_RandomAlpha_distance.m 890B

Partial_offloading_RandomAlpha.m 889B

bisection_partial_x.m 876B

Parameter_test.m 683B

Result.m 655B

master 194B

master 41B

README.md 60B

pack-972df2f84bfd5b1ccafa81e758d23b2b8777dbce.pack 63.27MB

packed-refs 114B

binary_partial_time_energy_VS_beta.png 12KB

binary_partial_time_energy_VS_distance.png 10KB

binary_partial_time_energy_VS_K.png 9KB

binary_cost_distance.png 9KB

binary_cost_K.png 9KB

DROO_convergence.png 9KB

binary_runTime_compare.png 8KB

binary_partial_cost_VS_K_beta.png 8KB

DROO_optimal_k.png 8KB

binary_Adaptive.png 7KB

partial_alpha.png 7KB

convex_beta.py 19KB

convex.py 19KB

dnn.py 8KB

Binary_offloading_LR.py 6KB

Binary_offloading_LR_beta.py 6KB

Binary_offloading_Adaptive.py 6KB

Binary_offloading_DROO.py 6KB

Binary_offloading_DROO_M100000.py 6KB

Binary_offloading_BCD.py 2KB

channel_generation.py 984B

optimization.cpython-36.pyc 6KB

convex.cpython-36.pyc 6KB

memory.cpython-36.pyc 6KB

dnn.cpython-36.pyc 6KB

pre-rebase.sample 5KB

fsmonitor-watchman.sample 5KB

update.sample 4KB

push-to-checkout.sample 3KB

pre-commit.sample 2KB

prepare-commit-msg.sample 1KB

pre-push.sample 1KB

commit-msg.sample 896B

pre-receive.sample 544B

applypatch-msg.sample 478B

pre-applypatch.sample 424B

pre-merge-commit.sample 416B

post-update.sample 189B

InputData_MonteCarlo100000_K8_Distance14.txt 42.92MB

Partial_offloading_BCD_beta_log.txt 22.57MB

Partial_offloading_BCD_distance_log.txt 19.17MB

Binary_LR_decision_MonteCarlo100000_K8_Distance14.txt 19.07MB

InputData_MonteCarlo30000_K10_Distance14.txt 15.74MB

InputData_MonteCarlo30000_K8_Distance10.txt 12.87MB

InputData_MonteCarlo30000_K8_Distance100.txt 12.87MB

InputData_MonteCarlo30000_K8_Distance8.txt 12.87MB

InputData_MonteCarlo30000_K8_Distance24.txt 12.87MB

InputData_MonteCarlo30000_K8_Distance22.txt 12.87MB

InputData_MonteCarlo30000_K8_Distance18.txt 12.87MB

InputData_MonteCarlo30000_K8_Distance14.txt 12.87MB

InputData_MonteCarlo30000_K8_Distance16.txt 12.87MB

InputData_MonteCarlo30000_K8_Distance20.txt 12.87MB

InputData_MonteCarlo30000_K8_Distance12.txt 12.87MB

InputData_MonteCarlo30000_K6_Distance14.txt 10.01MB

InputData_MonteCarlo30000_K4_Distance14.txt 7.15MB

InputData_MonteCarlo30000_K2_Distance14.txt 4.29MB

Binary_offloading_beta_VS_time_MonteCarlo10000_K8_Distance14.txt 3.1MB

Binary_offloading_beta_VS_energy_MonteCarlo10000_K8_Distance14.txt 3.1MB

Binary_LR_beta_VS_Time_MonteCarlo10000_K8_Distance14.txt 3.1MB

Binary_local_beta_VS_cost_MonteCarlo10000_K8_Distance14.txt 3.1MB

Binary_local_beta_VS_energy_MonteCarlo10000_K8_Distance14.txt 3.1MB

Binary_offloading_beta_VS_cost_MonteCarlo10000_K8_Distance14.txt 3.1MB

Binary_LR_beta_VS_energy_MonteCarlo10000_K8_Distance14.txt 3.1MB

共 328 条

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