Deep Reinforcement Learning 3 Practical Edition: Breakout

Aidemy　2020/11/22

Introduction

Hello, it is Yope! I'm a crunchy literary school, but I was interested in the possibilities of AI, so I went to the AI-specialized school "Aidemy" to study. I would like to share the knowledge gained here with you, and I am summarizing it on Qiita. I am very happy that many people have read the previous summary article. Thank you! This is the third post of deep reinforcement learning. Nice to meet you.

• This article is a summary of what you learned in "Aidemy" "in your own words". It may contain mistakes and misunderstandings. Please note.

What to learn this time ・ ・

Reinforcement learning practice by breaking blocks

Creating an environment

-Create an environment with the same method __ (gym.make ()) __ as in Chapter 2. In case of breakout, specify __ "BreakoutDeterministic-v4" __ as an argument. -The number of actions can be confirmed by __ "env.action_space.n" __.

·code

Model building

・ Here, a multi-layer neural network is constructed. Input is __ "4 frames of breakout screen" __. Also, in order to reduce the amount of calculation, the image is resized to __grayscale 84 × 84 pixels __. -The model uses Sequential (). As in Chapter 2, smooth the input with __Flatten () __, add the fully connected layer with Dense, and the activation function with Activation. -Since this time we are inputting an image (two-dimensional), we will use __ "Convolution2D ()" __, which is a two-dimensional convolution layer. The first argument is __ "filter" __, which specifies the number of dimensions __ of the output space, and the second argument is __ "kernel_size" __, the width and height of the collapsed __ window. Specify __. __ "strides" __ specifies the stride, that is, the width and height of the __ window that moves at one time __.

·code

History and policy settings

-Similar to Chapter 2, set __History __ and __Measures __ required to create an agent. -Use __ "SequentialMemory ()" __ for history. Specify limit and window_length as arguments. -Use __ "BoltzmannQPolicy ()" __ when using the Boltzmann policy, and __ "EpsGreedyQPolicy ()" __ when using the ε-greedy method. -Also, when changing the parameter ε to Linear, use __ "LinearAnnealedPolicy ()" __. When the argument is specified as shown in the code below, it means that the parameter ε is transformed into a linear line with a maximum of 1.0 and a minimum of 0.1 in 10 steps during training, and fixed at 0.05 during testing.

·code

Agent settings

-An agent can be created by passing model, memory, policy, nb_actions, nb_steps_warmup to the argument of __ "DQNAgent ()" __. After that, you can specify the learning method with __ "dqn.compile ()" __. Specify __optimization algorithm __ in the first argument and __ evaluation function __ in the second argument.

・ Code

Implementation of learning

・ After completing the settings in the previous section, learn using the DQN algorithm. __ "dqn.fit ()" __, specify the environment in the first argument and how many steps to learn in "nb_steps" in the second argument. -Also, the learning result can be saved in hdf5 format with __ "dqn.save_weights ()" __. Specify the file name in the first argument and whether to enable overwriting with "overwrite" in the second argument.

·code

Conducting the test

-Test with a trained agent. __ Do with "dqn.test ()" __. The argument is the same as fit, and the number of episodes "nb_episodes" is specified instead of the number of steps nb_steps. ・ By the way, in this breakout, it is one episode until the ball is dropped.

Dueling DQN

What is Dueling DQN?

-__Dueling DQN (DDQN) __ is an advanced version of DQN, which is a modification of the end of the DQN network layer. -In DQN, the Q value was output via the fully connected layer after the first three "convolution layers", but DDQN divides this __ fully connected layer into two , while the __ state. Outputs the value V and the other output action A. By finding the Q value from the last fully connected layer that takes these two as inputs, the performance is higher than DQN.

・ Figure

Implementation of Dueling DQN

-The implementation of Dueling DQN is the same as DQN until the addition of layers. It can be implemented by setting __ "enable_dueling_network = True" __ in the argument to __ (DQNAgent ()) __ when setting the agent and specifying the Q value calculation method __ "dueling_type" __. You can specify __ "'avg','max','naive'" __ for dueling_type.

・ Code

・ Result

Summary

-Even if you break a block, you can define the environment as in Chapter 2. -For model construction, this time we are using 2D image recognition, so we will use convolution. __ Use the "Convolution 2D" __ layer. -This policy uses the ε-greedy method, but the parameter ε needs to be changed linearly. In such a case, use __ "LinearAnnealedPolicy ()" __ to change __ linearly __. -Models that have been trained can be saved in hdf5 format by using __ "dqn.save_weights ()" __. -DuelingDQN is a DQN that divides the __total bond into two __, calculates the state value V and the action A respectively, and obtains the Q value from the two in the last layer. Implementation should specify __ "enable_dueling_network" __ and __ "dueling_type" __ in __DQNAgent () __.