def make_leg_model(leg, env):
leg_env = gym.make('PhantomXLeg-v0')
leg_env.set_info(env.info)
leg_env.leg_name = leg
policy = build_policy(leg_env, defaults['network'], **alg_kwargs)
model = ppo2.Model(policy=policy, ob_space=leg_env.observation_space, ac_space=leg_env.action_space, nbatch_act=nenvs,
nbatch_train=nbatch_train,
nsteps=defaults['nsteps'], ent_coef=defaults['ent_coef'], vf_coef=defaults['vf_coef'],
max_grad_norm=defaults['max_grad_norm'])
model.load('' + leg + '/checkpoints/05000')
return model
def runner(leg, env):
leg_env = gym.make('PhantomXLeg-v0')
leg_env.set_info(env.info)
leg_env.leg_name = leg
policy = build_policy(leg_env, defaults['network'], **alg_kwargs)
model = ppo2.Model(policy=policy,
ob_space=leg_env.observation_space,
ac_space=leg_env.action_space,
nbatch_act=nenvs,
nbatch_train=nbatch_train,
nsteps=defaults['nsteps'],
ent_coef=defaults['ent_coef'],
vf_coef=defaults['vf_coef'],
max_grad_norm=defaults['max_grad_norm'])
model.load('' + leg + '/checkpoints/05000')
obs = leg_env.reset()
ep_reward = 0
rewards = []
episode = 0
step = 0
while True:
step += 1
action, value_estimate, next_state, neglogp = model.step(obs)
obs, reward, done, _ = leg_env.step(action[0])
ep_reward += reward
if done:
leg_env.reset()
episode += 1
print(step)
print(ep_reward)
rewards.append(ep_reward)
step = 0
ep_reward = 0
if episode >= 100:
break
f = open(filename, "w+")
f.write("Variance: " + str(np.var(rewards)))
rewards = np.array(rewards, dtype=float)
f.write(",Median: " + str(statistics.median(rewards)))
f.write(",Mean: " + str(np.mean(rewards)))
f.close()
while True:
time.sleep(2)
print("DONE")
def runner(leg, env):
leg_env = gym.make('PhantomXLeg-v0')
leg_env.set_info(env.info)
leg_env.leg_name = leg
policy = build_policy(leg_env, defaults['network'], **alg_kwargs)
model = ppo2.Model(policy=policy,
ob_space=leg_env.observation_space,
ac_space=leg_env.action_space,
nbatch_act=nenvs,
nbatch_train=nbatch_train,
nsteps=defaults['nsteps'],
ent_coef=defaults['ent_coef'],
vf_coef=defaults['vf_coef'],
max_grad_norm=defaults['max_grad_norm'])
model.load(
'/tmp/training_data/dockerv1.3/PhantomX-v0/dppo2_mlp/2019-12-03_17h05min/'
+ leg + '/checkpoints/07000')
obs = leg_env.reset()
while True:
action, value_estimate, next_state, neglogp = model.step(obs)
obs, reward, done, _ = leg_env.step(action[0])
time.sleep(1 / 1000)
'num_layers': defaults['num_layers'],
'num_hidden': defaults['num_hidden']
}
policy = build_policy(env, defaults['network'], **alg_kwargs)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
nbatch = nenvs * defaults['nsteps']
nbatch_train = nbatch // defaults['nminibatches']
make_model = lambda: ppo2.Model(policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nbatch_act=nenvs,
nbatch_train=nbatch_train,
nsteps=defaults['nsteps'],
ent_coef=defaults['ent_coef'],
vf_coef=defaults['vf_coef'],
max_grad_norm=defaults['max_grad_norm'])
model = make_model()
if defaults['trained_path'] is not None:
model.load(defaults['trained_path'])
obs = env.reset()
loop = True
while loop:
actions = model.step_deterministic(obs)[0]
obs, reward, done, _ = env.step_runtime(actions)