def test(args=get_args()):
torch.set_num_threads(1) # we just need only one thread for NN
model_path = os.path.join(args.logdir, args.task, 'ddpg/policy.pth')
env = gym_make()
policy = load_policy(env, args.layer, model_path)
print(env.action_space.low, env.action_space.high)
obs = env.reset()
while True:
action = policy_forward(policy, obs, eps=0.05)
print(action)
obs, reward, done, info = env.step(action[0])
print(reward)
env.render()
if done:
break
def steer(self, from_node, to_node, t_max_extend=10.0, t_tree=5.0):
# using RL policy to steer from_node to to_node
parent_node = from_node
new_node = self.Node(from_node.state)
new_node.parent = parent_node
new_node.path.append(new_node.state)
state = new_node.state
goal = to_node.state.copy()
env = self.gym
env.state = state
env.goal = goal
obs = env._obs()
dt = 0.2
n_max_extend = round(t_max_extend / dt)
n_tree = round(t_tree / dt)
new_node_list = []
for n_extend in range(1, n_max_extend + 1):
action = policy_forward(self.policy, obs, eps=0.05)[0]
# control with RL policy
obs, rewards, done, info = env.step(action)
state = env.state
new_node.state = state
new_node.path.append(state)
if not env.robot_env.valid_state_check(state):
# collision
break
elif np.linalg.norm(state[:2] - goal[:2]) <= 1.0:
# reach
new_node_list.append(new_node)
break
elif n_extend % n_tree == 0:
new_node_list.append(new_node)
parent_node = new_node
new_node = self.Node(parent_node.state)
new_node.parent = parent_node
new_node.path.append(new_node.state)
self.node_list += new_node_list
return new_node_list
def test_policy():
# torch.set_num_threads(1) # we just need only one thread for NN
model_path = os.path.dirname(
__file__) + '/../data/net/end2end/ddpg/policy.pth'
test_env1 = pickle.load(
open(
os.path.dirname(__file__) + '/../data/obstacles/test_env1.pkl',
'rb'))
obs_list = pickle.load(
open(
os.path.dirname(__file__) + '/../data/obstacles/obs_list_list.pkl',
'rb'))[:1]
test_env2 = pickle.load(
open(
os.path.dirname(__file__) + '/../data/obstacles/test_env2.pkl',
'rb'))
training_env = pickle.load(
open(
os.path.dirname(__file__) + '/../data/obstacles/training_env.pkl',
'rb'))
env = DifferentialDriveGym(obs_list_list=[training_env])
env.reset()
policy = load_policy(env, [1024, 512, 512, 512], model_path)
obs = env.reset()
fig, ax = plt.subplots()
while True:
action = policy_forward(policy, obs, eps=0.05)
print(action)
obs, reward, done, info = env.step(action[0])
print(reward)
env.render()
if done:
break