def plan():
P = Planner(perfect_model)
observation = env.reset()
env.render()
# define start and goal states
start_state = [math.atan2(observation[1], observation[0]), observation[2]]
goal_state = [0.0, 0.0]
# plan a path using A*
path = P.find_path(start_state=start_state, goal_state=goal_state)
# action_seq = path[:, 2]
# action_seq = action_seq[:-10]
action_seq = path[1:, 2]
for ind, act in enumerate(action_seq):
ob, _, _, _ = env.step([act])
state = [math.atan2(ob[1], ob[0]), ob[2]]
print('Expected node ', path[ind + 1, :2])
print('Visited node ', P.state_to_node(state))
# print('\n')
env.render()
sleep(0.3)
def plan():
P = Planner(M.predict_using_model)
observation = env.reset()
env.render()
# define start and goal states
start_state = [math.atan2(observation[1], observation[0]), observation[2]]
goal_state = [0.0, 0.0]
# plan a path using A*
path = P.find_path(start_state=start_state, goal_state=goal_state)
action_seq = path[:, 2]
action_seq = action_seq[:-10]
for ind, act in enumerate(action_seq):
ob, _, _, _ = env.step([act])
state = [math.atan2(ob[1], ob[0]), ob[2]]
# print('Expected node ', path_seq[ind])
# print('Visited node ', P.state_to_node(state))
print('\n')
env.render()
sleep(0.2)
# Loop to replan from current state
while True:
P = Planner(M.predict_using_model)
start_state = state
path = P.find_path(start_state=start_state, goal_state=goal_state)
action_seq = path[:, 2]
action_seq = action_seq[:-10]
for ind, act in enumerate(action_seq):
ob, _, _, _ = env.step([act])
state = [math.atan2(ob[1], ob[0]), ob[2]]
# print('Expected node ', path_seq[ind])
# print('Visited node ', P.state_to_node(state))
print('\n')
env.render()
sleep(0.2)
def plan():
observation = env.reset()
env.render()
# define start and goal states
start_state = [math.atan2(observation[1], observation[0]), observation[2]]
goal_state = [0.0, 0.0]
scale_factor = 1
steps = []
scale_axis = []
scale_factor = 1
# plan a path using A*
for i in range(10):
P = Planner(perfect_model, scale_factor)
path, n_steps = P.find_path(start_state=start_state,
goal_state=goal_state)
if path is None:
print("Not able to compute path")
n_steps = 0
# action_seq = path[:, 2]
# action_seq = action_seq[:-10]
action_seq = path[1:, 2]
for ind, act in enumerate(action_seq):
ob, _, _, _ = env.step([act])
state = [math.atan2(ob[1], ob[0]), ob[2]]
# print('Expected node ', path[ind+1, :2])
# print('Visited node ', P.state_to_node(state))
# print('\n')
env.render()
scale_axis.append(scale_factor)
steps.append(n_steps)
print(i, scale_factor, n_steps)
scale_factor = scale_factor * 0.95
plt.plot(scale_axis, steps)
plt.xlabel("% discrete action-space")
plt.ylabel("No. steps")
plt.title("Discretization of action space vs no. of steps")
plt.show()