def main():
"""
Train on CartPole.
"""
args = pybullet_arg_parser().parse_args()
logger.configure(format_strs=['stdout', 'log', 'csv'],
log_suffix="Uber-GA-" + args.env + "_seed_" +
str(args.seed))
logger.log("Algorithm:Uber-GA-" + args.env + "_seed_" + str(args.seed))
env_id = args.env
seed = args.seed
generation = 0
with make_session() as sess:
env = make_pybullet_env(env_id, seed)
try:
model = simple_mlp(sess, env)
sess.run(tf.global_variables_initializer())
learn_sess = LearningSession(sess, model)
while True:
if generation >= 10000 or learn_sess.timesteps_so_far >= 5e6:
break
pop = learn_sess.generation(env,
trials=1,
population=POPULATION)
generation += 1
# rewards = [x[0] for x in pop]
# print('mean=%f best=%s' % (sum(rewards)/len(rewards), str(rewards[:10])))
finally:
env.close()
def main():
args = pybullet_arg_parser().parse_args()
logger.configure(format_strs=['stdout', 'log', 'csv'],
log_suffix="CMAES-" + args.env + "_seed_" +
str(args.seed))
logger.log("Algorithm: CMAES-" + args.env + "_seed_" + str(args.seed))
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
def main():
args = pybullet_arg_parser().parse_args()
logger.configure(
format_strs=['stdout', 'log', 'csv'], log_suffix = "NAC_Advantage_Fisher-"+args.env)
logger.log("Algorithm: NAC_Advantage_Fisher-"+args.env)
import random
args.seed += random.randint(0, 2**32 - 1)
logger.log("Algorithm: SEED-"+str(args.seed))
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
def main():
args = pybullet_arg_parser().parse_args()
logger.configure(format_strs=['stdout', 'log', 'csv'],
log_suffix="PES-S2-" + args.env + "_seed_" +
str(args.seed))
import random
seed = args.seed + random.randint(0, 2**32 - 1)
logger.log("Algorithm: PES-S2-" + args.env + "_seed_" + str(seed))
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
def main():
args = pybullet_arg_parser().parse_args()
logger.configure(format_strs=['stdout', 'log', 'csv'],
log_suffix="PPO-" + args.env)
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
def main():
args = pybullet_arg_parser().parse_args()
logger.configure(
format_strs = ['stdout', 'log', 'csv'], log_suffix = "PPO_Dual_NAC_Fisher-" + args.env)
logger.log("Algorithm: PPO_Dual_NAC_Fisher-" + args.env)
train(args.env, num_timesteps = args.num_timesteps, seed = args.seed)
