tensorflow reinforcement learning

TensorFlow and serving models of the product process. Serving Models in Production with TensorFlow serving: a systematic explanation of how to apply the TensorFlow serving model in a production environment. ML Toolkit: Introduces the use of TensorFlow machine learning libra

Smart Car self driving car + intensive learning reinforcement learning + neural network simulationHttps://github.com/MorvanZhou/my_research/tree/master/self_driving_research_DQNReinforcement learning for autonomous Driving obstacle avoidance using LIDARHttps://github.com/peteflorence/Machine-

background: Strengthening learning and playing games The simulator (model or emulator) outputs an image and an award with an action (action) as input. A single image does not fully understand the current state of the agent, so it has to combine the information of the action with the state sequence. The objective of the agent is to select actions in a certain way and intersect with the simulator to maximize future rewards. Bellman equation:Q∗ (s,a) =e

1 Preface In the previous depth Enhancement Study Series, we have analyzed the DQN algorithm in detail, a value based algorithm, then today, we are working with you to analyze another algorithm in depth enhancement learning, that is, based on the policy gradient policy gradient algorithm. The actor-critic algorithm combined with the value based algorithm is the most effective depth-enhanced learning algorit

following is a quote from the blog "Evolutionary Strategy optimization algorithm CEM (cross Entropy Method)" [3]. Cem can also be used to solve Markov decision-making processes, that is, to strengthen learning problems. We know that reinforcement learning is also a dynamic planning process in which an action is selected in a certain state as if a path is selecte

passage in paper:"We assume have access to a object detector that provides plausible object candidates."To be blunt is to give a target artificially. And then we'll train. (essentially nesting of two dqn)That's no point.This can be trained from the intuitive sense.But the meaning is relatively small.SummaryThis article is an exaggeration of the proposed level of DRL to solve the problem of sparse feedback, but in fact is not really a solution, the middle of the target is too artificial, not uni

"Not completed" Monte Carlo Monte Carlo is a kind of general algorithm, the idea is to approach the real by random sampling, here only introduced in the reinforcement learning application.The initial idea should be to run multiple cycles in succession, such as after two times (s, a), and calculates the corresponding GT, then Q (s,a) to take the average on it, but in fact, in order to optimize the strategy o

RL: Http://cdn.preterhuman.net/texts/science_and_technology/artificial_intelligence/Reinforcement%20Learning%20%20An%20Introduction%20-%20Richard%20S.%20Sutton%20,%20Andrew%20G.%20Barto.pdf Value ineration: 1. bertsekas, D. P., tsitsiklis, J. N. (1989). parallel and distributed computation: numerical methods. Prentice Hall. Republished by Athena scientific in 1997. 2. moore,. W ., Atkeson, C. g. (1993 ). prioritized sweeping:

1 Preface Deep reinforcement learning can be said to be the most advanced research direction in the field of depth learning, the goal of which is to make the robot have the ability of decision-making and motion control. The machine flexibility that human beings create is far lower than some low-level organisms, such as bees. DRL is to do this, but the key is to

Developing a complex depth learning model using Keras + TensorFlow This post was last edited by Oner at 2017-5-25 19:37Question guide: 1. Why Choose Keras. 2. How to install Keras and TensorFlow as the back end. 3. What is the Keras sequence model? 4. How to use the Keras to save and resume the pre-training model. 5. How to use the Keras API to develop VGG convol