[Reinforcement Learning] Get started to learn Sarsa(lambda λ) for reinforcement learning

Once you know what the Sarsa algorithm is, you can continue to learn Sarsa(lambda λ) algorithm.
I basically refer to these tutorial documents (written in Chinese) :
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/3-3-A-sarsa-lambda/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/3-3-tabular-sarsa-lambda/
https://zhuanlan.zhihu.com/p/28108498

The Sarsa(lambda λ) algorithm looks like this:

[Reinforcement Learning] Get started to learn Sarsa for reinforcement learning

If taking a look at Sarsa algorithm, you will find that it is so similar with Q-Learning.
For my previous post about Q-Learning, please refer to this link:
https://danny270degree.blogspot.com/2018/11/reinforcement-learning-get-started-to_21.html

Here is the Sarsa algorithm:

[Reinforcement Learning] Using dynamic programming to solve a simple GridWorld with 4X4

I borrow the example and its source code from here which is a dynamic programming to solve a simple GridWorld with 4X4 and put my explanation for the calculation of value function. Hope that will help to understand dynamic programming and Markov Reward Process(MRP) more quickly.

[Reinforcement Learning] Get started to learn DQN for reinforcement learning

The previous post about Q-Learning is here:
[Reinforcement Learning] Get started to learn Q-Learning for reinforcement learning

Basically, Deep Q-Learning ( DQN ) is upgraded the Q-Learning algorithm and the Q-table is replaced by the neural network. For the DQN tutorial, I refer to these as follows: ( sorry, they are written in Chinese )
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-1-A-DQN/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-1-DQN1/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-2-DQN2/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-3-DQN3/

[Reinforcement Learning] Get started to learn Q-Learning for reinforcement learning

The previous post about reinforcement learning:
[Reinforcement Learning] Get started to learn gradient method for reinforcement learning

For the Q-Learning tutorial, I refer to these as follows: ( sorry, they are written in Chinese )
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-2-A-q-learning/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-2-tabular-q1/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-3-tabular-q2/

[Reinforcement Learning] Get started to learn policy gradient method for reinforcement learning

This post is about my first time to learn policy gradient method for reinforcement learning. Basically, there are already a lot of materials on the internet, but in this time, I only want to focus on a tutorial as follows: ( sorry, they are written in Chinese )
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/5-1-policy-gradient-softmax1/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/5-2-policy-gradient-softmax2/

[RNN] What are the difference of input and output's tensor shape in dynamic_rnn and static_rnn using TensorFlow

When studying RNN, my first issue encountered in my program is about the shape of input and output tensors. Shape is a very important information to connect between layers. Here I just directly point out what are differences in input/output shape of static RNN and dynamic RNN.
P.S: If you use Keras to write your RNN model, you won't need to deal with these details.

[TensorFlow] The explanation of average gradients by example in data parallelism

When studying some examples of training model using Multi-GPUs ( in data parallelism ), the average gradients function always exists in some kind of ways, and here is a simple version as follows: