Repository for codes of 'Deep Reinforcement Learning'
This repository is the codes for Deep Reinforcement Learning
I verified my codes with games. The games are made with pygame
. I made the games or I modified them to apply for DRL.
Also, environments, which are made by Unity ML-agents
, are in Unity_ML_Agent Repository
Performance of each algorithm that I implemented are as follows (verified with Breakout
).
Youtube Link of Playing Breakout with NoisyNet DQN
I set up the DRL code as follows.
This is the PPT file for the description of DQN codes
that I implemented.
Description of algorithms link
Software
Hardware
CPU: Intel(R) Core(TM) i7-4790K CPU @ 4.00GHZ
GPU: GeForce GTX 1080TI
Memory: 16GB
The sample code for my environment is Environment_sample.py
# This is sample code for Deep Reinforcement Learning testing environment
# Import modules
import sys
import numpy as np
import random
# Import games
sys.path.append("DQN_GAMES/")
# add as game the one that you want to play!!
import pong as game
import dot
import dot_test
import tetris
import wormy
import breakout
# Get Number of action and name of game from the game code
Num_action = game.Return_Num_Action()
game_name = game.ReturnName()
# Get game state class from game code
game_state = game.GameState()
while True:
# Choose random action
action = np.zeros([Num_action])
action[random.randint(0, Num_action - 1)] = 1.0
# You can get next observation, reward and terminal after action
observation_next, reward, terminal = game_state.frame_step(action)
You can run the game with random action using this code!!
Please check that you made the folder for saved variables
checkpoint = tf.train.get_checkpoint_state("saved_networks_DQN")
Then you are ready to implement the code! :laughing:
Most of the games are made with python and pygame!
The codes are easy to understand and variables are easy to understand.
So fix the code as you want!! :laughing: