values_filename = "pong_values.dat"
if os.path.exists(values_filename):
os.remove(values_filename)
if os.path.exists(policy_filename):
os.remove(policy_filename)
alpha = 0.05 # values / critic learning parameter
beta = 0.01 # actor learning parameter
gamma = 0.5 # error signal: future states parameter
world_dim = env.getWorldDim()
num_possible_moves = env.getActionDim()
state = env.getState()
iterations = int(sys.argv[1])
pol_file = None
val_file = None
if os.path.exists(policy_filename):
pol_file = open(policy_filename, 'r+')
policy = numpy.array(json.loads(pol_file.read()))
pol_file.close()
else:
#create random policy
policy = numpy.random.rand(world_dim[1], world_dim[0], num_possible_moves)
pol_file = open(policy_filename, 'w+')
import os import json #import pong_environment as env import pong_environment_training as env policy_filename = "pong_policy.dat" values_filename = "pong_values.dat" alpha = 0.1 # values / critic learning parameter beta = 0.01 # actor learning parameter gamma = 0.5 # error signal: future states parameter world_dim = env.getWorldDim() num_possible_moves = env.getActionDim() state = env.getState() iterations = int(sys.argv[1]) pol_file = None val_file = None if os.path.exists(policy_filename): pol_file = open(policy_filename, 'r+') policy = numpy.array(json.loads(pol_file.read())) pol_file.close() else: #create random policy policy = numpy.random.rand(world_dim[1], world_dim[0], num_possible_moves) pol_file = open(policy_filename, 'w+')
