cur_r = int(args.cur_r)
load_buffer = args.load_buffer
my_tqdm = tqdm(total=total_steps, dynamic_ncols=True)
print('starting loop, {} loops left'.format(total_loop))
if not args.vm :
from gym.envs.classic_control.rendering import SimpleImageViewer
eye_viewer = SimpleImageViewer(maxwidth=1500)
bar = np.ones((5,3),dtype=np.uint8)*np.array([255,255,0],dtype=np.uint8)
# For benchmark
st = time.time()
env = gym.make(ENVIRONMENT)
bef_o = env.reset()
if args.load :
player = Player(env.observation_space, env.action_space, my_tqdm,
args.load, args.log_name, cur_loop*total_steps, cur_r, load_buffer)
elif args.log_name:
# If log directory is explicitely selected
player = Player(env.observation_space, env.action_space, my_tqdm,
log_name=args.log_name)
else :
player = Player(env.observation_space, env.action_space, my_tqdm)
if not args.vm :
env.render()
for step in range(total_steps):
action = player.act(bef_o)
aft_o,r,d,i = env.step(action)
player.step(bef_o,action,r,d,i)
if d :
bef_o = env.reset()
else:
from gym.envs.classic_control.rendering import SimpleImageViewer
eye_viewer = SimpleImageViewer(maxwidth=1500)
bar = np.ones((5,3),dtype=np.uint8)*np.array([255,255,0],dtype=np.uint8)
# For benchmark
st = time.time()
env = tools.EnvWrapper(gym.make(ENVIRONMENT, **env_kwargs))
eval_env = tools.EnvWrapper(gym.make(ENVIRONMENT, **env_kwargs))
bef_o = env.reset()
if args.log_name:
# If log directory is explicitely selected
player = Player(
observation_space= env.observation_space,
action_space= env.action_space,
model_f= model_f,
tqdm= my_tqdm,
log_name= args.log_name
)
else :
player = Player(
observation_space= env.observation_space,
action_space= env.action_space,
model_f= model_f,
tqdm= my_tqdm,
)
if args.render :
env.render()
if args.profile:
# Warm up
load_buffer = args.load_buffer
buf_count = int(args.buf_count)
print('starting loop, {} loops left'.format(total_loop))
if not args.vm:
from gym.envs.classic_control.rendering import SimpleImageViewer
eye_viewer = SimpleImageViewer(maxwidth=1500)
bar = np.ones(
(5, 3), dtype=np.uint8) * np.array([255, 255, 0], dtype=np.uint8)
# For benchmark
st = time.time()
env = gym.make('mouseCl-v0')
o = env.reset()
if args.load:
player = Player(env.observation_space, env.action_space, args.load,
args.log_name, cur_loop * total_steps, cur_r, buf_full,
load_buffer, buf_count)
else:
player = Player(env.observation_space, env.action_space)
if not args.vm:
env.render()
for step in trange(total_steps, ncols=50):
action = player.act(o)
o, r, d, i = env.step(action)
player.step(o, r, d, i)
if d:
o = env.reset()
if not args.vm:
env.render()
next_save = player.save_model()
# For benchmark
st = time.time()
env = gym.make(ENVIRONMENT, **env_kwargs)
if hp.CLASSIC:
env = tools.EnvWrapper(env)
last_obs = env.reset()
render = args.render
if render :
env.render()
player = Player(
observation_space= env.observation_space,
action_space= env.action_space,
model_f= model_f,
m_dir=args.load,
log_name= args.log_name,
mixed_float=args.mixed_float,
)
need_to_eval = False
buf = ReplayBuffer(env.observation_space, env.action_space)
reset_buffer = True
cum_reward = 0.0
rounds = 0
per_round_steps = 0
act_steps = 0
if args.profile:
for step in range(20):
last_obs, cum_reward, rounds, act_steps,\
eye_viewer = SimpleImageViewer(maxwidth=1500)
bar = np.ones(
(5, 3), dtype=np.uint8) * np.array([255, 255, 0], dtype=np.uint8)
# For benchmark
st = time.time()
env = gym.make(ENVIRONMENT, **env_kwargs)
bef_o = env.reset()
if args.load:
player = Player(
observation_space=env.observation_space,
action_space=env.action_space,
model_f=model_f,
tqdm=my_tqdm,
m_dir=args.load,
log_name=args.log_name,
start_step=cur_loop * loop_steps,
start_round=cur_r,
load_buffer=load_buffer,
)
elif args.log_name:
# If log directory is explicitely selected
player = Player(observation_space=env.observation_space,
action_space=env.action_space,
model_f=model_f,
tqdm=my_tqdm,
log_name=args.log_name)
else:
player = Player(
observation_space=env.observation_space,
parser = argparse.ArgumentParser()
parser.add_argument('-pf', dest='profile', action='store_true', default=False)
args = parser.parse_args()
hp.Buffer_size = 500
hp.Learn_start = 200
hp.Batch_size = 32
hp.Target_update = 500
hp.epsilon = 1
hp.epsilon_min = 0.01
hp.epsilon_nstep = 500
original_env = gym.make('mouseCl-v0')
test_env = EnvTest(original_env.observation_space)
player = Player(original_env.observation_space, test_env.action_space)
o = test_env.reset()
# for step in trange(1000) :
# player.act(o,training=True)
# if step%5 == 0 :
# action = 2
# elif step%5 == 1 :
# action = 1
# elif step%5 == 2 :
# action = 2
# elif step%5 == 3 :
# action = 1
# elif step%5 == 4 :
# action = 0
# o, r, d, i = test_env.step(action)
# player.step(action, r,d,i)
cur_loop = int(args.cur_loop)
cur_r = int(args.cur_r)
load_buffer = args.load_buffer
print('starting loop, {} loops left'.format(total_loop))
if not args.vm:
from gym.envs.classic_control.rendering import SimpleImageViewer
eye_viewer = SimpleImageViewer(maxwidth=1500)
bar = np.ones(
(5, 3), dtype=np.uint8) * np.array([255, 255, 0], dtype=np.uint8)
# For benchmark
st = time.time()
env = gym.make(ENVIRONMENT)
o = env.reset()
if args.load:
player = Player(env.observation_space, env.action_space, args.load,
args.log_name, cur_loop * total_steps, cur_r, load_buffer)
elif args.log_name:
# If log directory is explicitely selected
player = Player(env.observation_space,
env.action_space,
log_name=args.log_name)
else:
player = Player(env.observation_space, env.action_space)
if not args.vm:
env.render()
for step in trange(total_steps, ncols=80):
action = player.act(o, training=True)
o, r, d, i = env.step(action)
player.step(action, r, d, i)
if d:
o = env.reset()