def test(args):
print('start testing')
ddpg = DDPG()
ddpg.load_model(args.model, load_memory=False)
env = RunEnv(visualize=args.visualize, max_obstacles=args.max_obs)
np.random.seed(args.seed)
for i in range(1):
step = 0
state = env.reset(difficulty=2)
fg = FeatureGenerator()
state = fg.gen(state)
#obs = fg.traj[0]
#print(obs.left_knee_r, obs.right_knee_r)
ep_reward = 0
ep_memories = []
while True:
action = ddpg.select_action(list(state))
next_state, reward, done, info = env.step(action.tolist())
next_state = fg.gen(next_state)
#obs = fg.traj[0]
#print(obs.left_knee_r, obs.right_knee_r)
print('step: {0:03d}'.format(step), end=', action: ')
for act in action:
print('{0:.3f}'.format(act), end=', ')
print()
state = next_state
ep_reward += reward
step += 1
print('reward:', ep_reward)
if done:
break
print('\nEpisode: {} Reward: {}, n_steps: {}'.format(
i, ep_reward, step))
def submit(args):
print('start submitting')
remote_base = 'http://grader.crowdai.org:1733'
client = Client(remote_base)
ddpg = DDPG()
ddpg.load_model(args.model, load_memory=False)
state = client.env_create(TOKEN)
fg = FeatureGenerator()
state = fg.gen(state)
step = 0
ep_reward = 0
while True:
print('selecting action ...', end=' ')
action = ddpg.select_action(list(state))
print('client.env_step ...')
next_state, reward, done, info = client.env_step(action.tolist())
next_state = fg.gen(next_state)
print('step: {0:03d}, ep_reward: {1:02.08f}'.format(step, ep_reward))
state = next_state
ep_reward += reward
step += 1
if done:
print('done')
state = client.env_reset()
if not state:
break
step = 0
ep_reward = 0
fg = FeatureGenerator()
state = fg.gen(state)
client.submit()