#This file contains all program parameters


""" ###########
MAIN PARAMETERS
########### """
#M stands for main
M_NUM_GAMES             = 10000                            #number of games to be played in total.
M_MAX_FRAMES_PER_GAME   = 12000                        #maximum amount of frames allowed per game, after which it will quit that game.
M_DO_RENDER_GAME        = True                          #set to true if you want to see the visuals of the game
M_PLAY_BATCH_SIZE       = 32                             #an agent replays this many state  games from strored states. size of how many games are played



""" ##########
RESULTS PARAMETERS
############## """
#R stands for results
R_AVG_RANGE     = 1                            # Take the average result over N individual game results
R_PLOTS_PATH    = 'plots/'                      # Directory to place the plots in
R_PLOTS_FILE    = 'reward_results_new.eps'          # File name for result plots

""" ##########
NETWORK PARAMS
########## """
#N stands for neural
N_MODEL_FILE_PATH       = 'saved_models/'               #path for saving the model
N_MODEL_FILE_NAME       = 'model.h5'                    #file name for saving the model


""" #############
Q_LEARNING PARAMS
############# """
#all parameters that have to do with q-learning
Q_MAX_EPSILON           = 1.0                           #maximum value for a random action in the search space
Q_MIN_EPSILON           = 0.1                          #minimum probability for a random chance in search space (example if 0.05, then there is a 5% chance of taking an exploratory move)
Q_DECAY_EPSILON         = 0.999                         #The maximum epsilon value is multiplied by this number to get a lower epsilon value. (so less chance of a exploratory move happing as max epsilon decreases)
Q_MAX_STATES_RETAINED   = 20000                          #The maximum number of states stored in the agents state memory list
Q_GAMMA                 = 0.99                          #The discount value of state. How much the valuation of state is discounted. Gamma determines the importance of future rewards.
Q_LEARNING_RATE         = 0.001                         #Learning rate for the q-learning algorithm
P_TAU                   = 1.0
f800ba3d181a6aa903a4b72b8d0f3d166e440676
<mask token>

<mask token>
M_NUM_GAMES = 10000
M_MAX_FRAMES_PER_GAME = 12000
M_DO_RENDER_GAME = True
M_PLAY_BATCH_SIZE = 32
<mask token>
R_AVG_RANGE = 1
R_PLOTS_PATH = 'plots/'
R_PLOTS_FILE = 'reward_results_new.eps'
<mask token>
N_MODEL_FILE_PATH = 'saved_models/'
N_MODEL_FILE_NAME = 'model.h5'
<mask token>
Q_MAX_EPSILON = 1.0
Q_MIN_EPSILON = 0.1
Q_DECAY_EPSILON = 0.999
Q_MAX_STATES_RETAINED = 20000
Q_GAMMA = 0.99
Q_LEARNING_RATE = 0.001
P_TAU = 1.0

#This file contains all program parameters


""" ###########
MAIN PARAMETERS
########### """
#M stands for main
M_NUM_GAMES             = 10000                            #number of games to be played in total.
M_MAX_FRAMES_PER_GAME   = 12000                        #maximum amount of frames allowed per game, after which it will quit that game.
M_DO_RENDER_GAME        = True                          #set to true if you want to see the visuals of the game
M_PLAY_BATCH_SIZE       = 32                             #an agent replays this many state  games from strored states. size of how many games are played



""" ##########
RESULTS PARAMETERS
############## """
#R stands for results
R_AVG_RANGE     = 1                            # Take the average result over N individual game results
R_PLOTS_PATH    = 'plots/'                      # Directory to place the plots in
R_PLOTS_FILE    = 'reward_results_new.eps'          # File name for result plots

""" ##########
NETWORK PARAMS
########## """
#N stands for neural
N_MODEL_FILE_PATH       = 'saved_models/'               #path for saving the model
N_MODEL_FILE_NAME       = 'model.h5'                    #file name for saving the model


""" #############
Q_LEARNING PARAMS
############# """
#all parameters that have to do with q-learning
Q_MAX_EPSILON           = 1.0                           #maximum value for a random action in the search space
Q_MIN_EPSILON           = 0.1                          #minimum probability for a random chance in search space (example if 0.05, then there is a 5% chance of taking an exploratory move)
Q_DECAY_EPSILON         = 0.999                         #The maximum epsilon value is multiplied by this number to get a lower epsilon value. (so less chance of a exploratory move happing as max epsilon decreases)
Q_MAX_STATES_RETAINED   = 20000                          #The maximum number of states stored in the agents state memory list
Q_GAMMA                 = 0.99                          #The discount value of state. How much the valuation of state is discounted. Gamma determines the importance of future rewards.
Q_LEARNING_RATE         = 0.001                         #Learning rate for the q-learning algorithm
P_TAU                   = 1.0