tensorflow reinforcement learning

In reinforcement learning (v) using the sequential Difference method (TD), we discuss the method of solving the reinforcement learning prediction problem by using time series difference, but the solving process of the control algorithm is not in-depth, this paper gives a detailed discussion on the on-line control algor

Reinforcement LearningThe solution to the problem of control decision: to design a return function (reward functions), if the learning agent (such as the above four-legged robot, chess AI program) in the decision of a step, to obtain a better result, Then we give the agent some return (such as the return function result is positive), get poor results, then the return function is negative. For example, a qua

From:http://wanghaitao8118.blog.163.com/blog/static/13986977220153811210319/Accessed 2016-03-10Intensive Learning (deep reinforcement learning) resourcesGoogle's deep-mind team published a bull X-ray article in Nips in 2013, which blinded many people and unfortunately I was in it. Some time ago collected a lot of information about this, has been lying in the coll

Original address machine learning in the Cloud, with TensorFlowWednesday, MarchPosted by Slaven Bilac, software Engineer, Google analyticsmachine learning in the cloud with TensorFlowat Google, researchers collaborate closely and product teams, applying the latest advances in machine learning to Exi Sting products and Services-such asSpeech recognition in the Goo

Deep reinforcement learning with Double q-learningGoogle DeepMind　　AbstractThe mainstream q-learning algorithm is too high to estimate the action value under certain conditions. In fact, it was not known whether such overestimation was common, detrimental to performance, and whether it could be organized from the main body. This article answers the above question

1. A series of articles about getting started with DQN:DQN from getting started to giving up2. Introductory Paper2.1 Playing Atariwith a deep reinforcement learning DeepMind published in Nips 2013, the first time in this paper Reinforcement learning this name, and proposed DQN (deep q-network) algorithm, realized from

Bellman equation is a solution to the ideal condition, and these methods are the achievable methods that are formed by abandoning the ideal accuracy.SummaryThis paper combs several TD-related algorithms. TD Algorithms in particular t d ( λ ) The method leads to the eligibility trace (the translation does not know whether the qualification trail), this part of the content to be analyzed later.StatementThe pictures of this article are captured from:1

"Learn the basics of learning in simplified learning notes" 4. Reinforcement learning method without model-Monte Carlo algorithm Explain again what is no model. No model is the state transfer function, the return function does not know the situation.In the model-based dynamic programming method, which is based on mode

Deep Q Network 4.1 DQN Algorithm Update 4.2 DQN Neural Network 4.3 DQN thinking decision 4.4 OpenAI Gym Environment Library Notesdeep q-learning algorithmThis gives us the final deep q-learning algorithm with experience Replay:There is many more tricks this DeepMind used to actually make it work–like target network, error clipping, reward Clipp ing etc, but these is out of the scop

introduces the exploration of the user growth group based on the TensorFlow serving in the deep learning line, locates, analyzes and solves the performance problem, and finally realizes the online service with high performance, strong stability and support of various deep learning models.With a complete offline training and on-line predictive Framework Foundatio

LinkHttps://www.quora.com/What-are-the-best-books-about-reinforcement-learningThe main RL problems is related to:-Information Representation:from POMDP to predictive state representation to deep-learning to Td-networks-Inverse rl:how To learn the reward?-Algorithms+ Off-policy+ Large Scale:linear and nonlinear approximations of the value function+ Policy Search vs. Q-le