class Environment:
def __init__(self):
print("Setting env...")
self.env = RunEnv(visualize=False)
print("Env set !")
def get_state_size(self):
return list(self.env.observation_space.shape)
def get_action_size(self):
return self.env.action_space.shape[0]
def get_bounds(self):
return self.env.action_space.low, self.env.action_space.high
def set_render(self, render):
self.env = RunEnv(visualize=render)
def reset(self):
return self.env.reset(difficulty=0)
def random(self):
return self.env.action_space.sample()
def act(self, action):
return self.env.step(action)
def close(self):
self.env.close()
class Environment:
def __init__(self):
self.env = RunEnv(visualize=False)
print()
self.render = False
def get_state_size(self):
return list(self.env.observation_space.shape)
def get_action_size(self):
return self.env.action_space.shape[0]
def get_bounds(self):
return self.env.action_space.low, self.env.action_space.high
def set_render(self, render):
visu = render and DISPLAY
if visu != self.render:
self.render = visu
self.env = RunEnv(visualize=visu)
self.reset()
def reset(self):
return np.asarray(self.env.reset(difficulty=0))
def random(self):
return self.env.action_space.sample()
def act(self, action):
s_, r, d, i = self.env.step(action)
return np.asarray(s_), r, d, i
def close(self):
self.env.close()
class LearnToRunEnv(gym.Env):
"""Wrapping LearnToRunEnv in OpenAI Gym"""
def __init__(self, visualize=False, difficulty=None):
super(LearnToRunEnv, self).__init__()
if difficulty == None:
self.difficulty = random.randint(0,2)
else:
self.difficulty = difficulty
self.learntorun_env = RunEnv(visualize=visualize)
self.observation_space = self.learntorun_env.observation_space
self.action_space = self.learntorun_env.action_space
self._spec = EnvSpec("RunEnv-diff{}-v1".format(difficulty))
def _step(self, action):
obs, reward, terminal, info = self.learntorun_env.step(action)
return np.asarray(obs), reward, terminal, info
def _reset(self):
obs = self.learntorun_env.reset(difficulty=self.difficulty,\
seed=self.learntorun_seed)
return np.asarray(obs)
def _render(self, mode='human', close=False):
#raise NotImplementedError
return None
def _seed(self, seed=None):
self.learntorun_seed = seed
def _close(self):
self.learntorun_env.close()
class LearnToRunEnv(gym.Env):
"""Wrapping LearnToRunEnv in OpenAI Gym"""
def __init__(self, visualize=False, difficulty=None):
super(LearnToRunEnv, self).__init__()
if difficulty == None:
self.difficulty = random.randint(0, 2)
else:
self.difficulty = difficulty
self.learntorun_env = RunEnv(visualize=visualize)
self.observation_space = self.learntorun_env.observation_space
self.action_space = self.learntorun_env.action_space
def _step(self, action):
return self.learntorun_env.step(action)
def _reset(self):
return self.learntorun_env.reset(difficulty=self.difficulty,\
seed=self.learntorun_seed)
def _render(self, mode='human', close=False):
#raise NotImplementedError
return None
def _seed(self, seed=None):
self.learntorun_seed = seed
def _close(self):
self.learntorun_env.close()
def standalone_headless_isolated(conn,
visualize,
n_obstacles,
run_logs_dir,
additional_info,
higher_pelvis=0.65):
try:
e = RunEnv(visualize=visualize, max_obstacles=n_obstacles)
if higher_pelvis != 0.65:
bind_alternative_pelvis_judgement(e, higher_pelvis)
e = MyRunEnvLogger(e,
log_dir=run_logs_dir,
additional_info=additional_info)
while True:
msg = conn.recv()
# messages should be tuples,
# msg[0] should be string
if msg[0] == 'reset':
o = e.reset(difficulty=msg[1], seed=msg[2])
conn.send(o)
elif msg[0] == 'step':
ordi = e.step(msg[1])
conn.send(ordi)
elif msg[0] == 'close':
e.close()
conn.send(None)
import psutil
current_process = psutil.Process()
children = current_process.children(recursive=True)
for child in children:
child.terminate()
return
except Exception as e:
import traceback
print(traceback.format_exc())
conn.send(e)
def main():
env = RunEnv(visualize=True)
env.close()
with open('save.p', 'r') as f:
population = pickle.load(f)
nn = population[0][0]
total_reward = 0
observation = env.reset()
total_reward = 0
observation = env.reset()
for i in range(200):
step = nn.compute(i)
observation, reward, done, info = env.step(step)
total_reward += reward
if done:
break
print total_reward
max_timesteps=args.steps,
timesteps_per_batch=args.batch,
clip_param=args.clip,
entcoeff=args.ent,
optim_epochs=args.epochs,
optim_stepsize=args.stepsize,
optim_batchsize=args.optim_batch,
adam_epsilon=1e-5,
gamma=args.gamma,
lam=0.95,
schedule=args.schedule,
callback=on_iteration_start,
verbose=args.verbose,
)
env.close()
if MPI.COMM_WORLD.Get_rank() == 0:
plot_history(history)
save_model()
if args.repeat:
cmd = 'python run_osim.py --repeat --train --model %s --steps %s --size %s' % (args.model, args.steps, args.size)
subprocess.call(cmd.split(' '))
if args.test:
observation = env.reset()
observation = preprocess(observation, step=1, verbose=args.verbose)
pi = policy_fn('pi', env.observation_space, env.action_space)
if not load_model():
