Proximal_Policy_Optimization

所属分类:人工智能/神经网络/深度学习
开发工具:Python
文件大小:8KB
下载次数:18
上传日期:2019-12-04 10:54:55
上 传 者小人物0104
说明:  强化学习可以按照方法学习策略来划分成基于值和基于策略两种。而在深度强化学习领域将深度学习与基于值的Q-Learning算法相结合产生了DQN算法,通过经验回放池与目标网络成功的将深度学习算法引入了强化学习算法。
(Reinforcement learning can be divided into value-based learning and strategy based learning according to method learning strategies. In the field of deep reinforcement learning, dqn algorithm is generated by combining deep learning with value-based Q-learning algorithm. Through experience playback pool and target network, deep learning algorithm is successfully introduced into reinforcement learning algorithm.)

文件列表:
Proximal_Policy_Optimization (0, 2019-04-08)
Proximal_Policy_Optimization\discrete_DPPO.py (8808, 2019-01-21)
Proximal_Policy_Optimization\DPPO.py (8270, 2019-01-21)
Proximal_Policy_Optimization\simply_PPO.py (6458, 2019-01-21)

近期下载者

相关文件


收藏者