import gym
from agent import QAgent
env = gym.make('CartPole-v0')
agent = QAgent(env)
agent.train()
t = agent.run()
print("Time", t)
#create env
env = gym.make(ENV_NAME)
print(env.unwrapped.spec.id)
#create agent
model = QTable(nostates=env.observation_space.n,
noactions=env.action_space.n,
learning_rate=LEARNING_RATE,
discount_factor=DISCOUNT_FACTOR)
agent = QAgent(actions=env.action_space.n,
expl_max=EXPLORATION_MAX,
expl_min=EXPLORATION_MIN,
expl_decay=EXPLORATION_DECAY,
model=model)
#get and parse user args
args = Parser.parseargs(defaultTrainIterations=10000,
defaultEvalIterations=10)
if args.load:
agent.load(env, args.loadversion)
if args.train != 0:
agent.train(env,
iterations=args.train,
train_s=1,
save_i=SAVE_MODEL_EVERY)
if args.eval != 0:
print("Evaluation results (lower scores are better):")
agent.evaluate(env, args.eval)
if args.save:
agent.save(env, args.saveversion)
#close env
env.close()