DarrenXf的专栏

Soft Actor-Critic Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 1801.01290论文地址https://arxiv.org/abs/1801.01290个人翻译，并不权威Tuomas HaarnojaAurick ZhouPieter AbbeelSergey LevineAbstract 摘要无模型深度强化学习(RL)算法Model-free deep

Proximal Policy Optimization Algorithms 近端策略优化算法论文地址https://arxiv.org/abs/1707.06347个人翻译，并不权威John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg KlimovOpenAI{joschu, filip, prafulla, alec, oleg}@openai.comABSTRACT 摘要我们提出了一种新的强化学习策略梯

CONTINOUS CONTROL WITH DEEP REINFORCEMENT LEARNING论文地址https://arxiv.org/abs/1509.02971个人翻译，并不权威Timothy P.Lilicrp,Jonathan J.Hunt,Alexander Pritzel, Nicolas Heess,Tom Erez, Yuval Tassa, David Silver & Daan WierstraGoogle DeepmindLondon,UK{count

Playing Atari with Deep Reinfocement Learning论文地址https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf个人翻译，并不权威深度强化学习玩Atari摘要我们提出了第一个成功通过强化学习直接从高维感官输入中学习控制策略的深度学习模型。该模型是一个卷积神经网络，用Q-learing的变种训练，输入为原始像素，输出为估计未来reward的值函数。我们将我们的方法应用于七个来自街机学习环境的Atari 2600游戏

key_papers_in_deep_rlWhat follows is a list of papers in deep RL that are worth reading. This is far from comprehensive, but should provide a useful starting point for someone looking to do research ...