import gym
import Agent
import random
import numpy as np
def RandomActionDiscrete(ActionProp):
ActionSize = len(ActionProp)
Action = np.random.choice(np.arange(ActionSize), p=ActionProp)
ActionOut = np.zeros(ActionSize)
ActionOut[Action] = 1
return Action, ActionOut
env = gym.make("CartPole-v1")
Ag = Agent.ACPGAg(2, 4)
EpisodeTime = 200
CurrentEpisode = 0
DoneTimes = 0
while True:
Observation = env.reset()
O_array = []
R_array = []
A_array = []
AccReward = 0
CurrentEpisode += 1
if CurrentEpisode % 50 == 0:
print(DoneTimes)
DoneTimes = 0
for t in range(EpisodeTime):
import random
import numpy as np
def RandomActionDiscrete(ActionProp):
ActionSize = len(ActionProp)
ActionOut = np.zeros(ActionSize)
rand = random.uniform(0,1)
for ActionIndex in range(ActionSize):
if rand < ActionProp[ActionIndex]:
ActionOut[ActionIndex] = 1
return ActionIndex, ActionOut
ActionOut[ActionSize-1] = 1
return ActionSize-1, ActionOut
env = gym.make("Acrobot-v1")
Ag = Agent.ACPGAg(3, 6)
CurrentEpisode = 0
while True:
Observation = env.reset()
O_array = []
R_array = []
A_array = []
CurrentEpisode += 1
rememberTimeStep = 0
StepTime = 0
score = 0
while True:
StepTime += 1
if CurrentEpisode%100 == 0:
env.render()
