def get_active_exp(env, threshold, ae, xm, xs, render, take_max = False, max_act_steps = 20):
err_avg = 0
for i in range(20):
state = env.reset()
state = robot_reset(env)
error = ae.error((np.concatenate((state["observation"],
state["achieved_goal"],
state["desired_goal"])).reshape((1,-1)) - xm)/xs)
err_avg+=error
err_avg/=20
state = env.reset()
error = ae.error((np.concatenate((state["observation"],
state["achieved_goal"],
state["desired_goal"])).reshape((1,-1)) - xm)/xs)
#print("predicted error", error)
if not take_max:
tried = 0
while not error > threshold*err_avg:
tried+=1
state = env.reset()
state = robot_reset(env)
error = ae.error((np.concatenate((state["observation"],
state["achieved_goal"],
state["desired_goal"])).reshape((1,-1)) - xm)/xs)
# print("predicted error", error.numpy(), err_avg.numpy())
# print("Tried ", tried, " initial states")
new_states, new_acts = man_controller.get_demo(env, state, CTRL_NORM, render)
return new_states, new_acts
else:
errs_states = []
for k in range(max_act_steps):
state = env.reset()
state = robot_reset(env)
error = ae.error((np.concatenate((state["observation"],
state["achieved_goal"],
state["desired_goal"])).reshape((1,-1)) - xm)/xs)
s, g = utils.save_state(env)
errs_states.append([s, g, error])
max_error = -1000
max_key = ()
for el in range(len(errs_states)):
if errs_states[el][2] > max_error:
max_error = errs_states[el][2]
max_key = el
new_env = utils.set_state(env, errs_states[max_key][0], errs_states[max_key][1])
state, *_ = new_env.step(np.zeros(4))
new_states, new_acts = man_controller.get_demo(new_env, state, CTRL_NORM, render)
return new_states, new_acts
def get_active_exp(env, threshold, ae, xm, xs, render):
err_avg = 0
for i in range(20):
state = env.reset()
error = ae.error((np.concatenate(
(state["observation"], state["achieved_goal"],
state["desired_goal"])).reshape((1, -1)) - xm) / xs)
err_avg += error
err_avg /= 20
state = env.reset()
error = ae.error((np.concatenate(
(state["observation"], state["achieved_goal"],
state["desired_goal"])).reshape((1, -1)) - xm) / xs)
#print("predicted error", error)
tried = 0
while not error > threshold * err_avg:
tried += 1
state = env.reset()
error = ae.error((np.concatenate(
(state["observation"], state["achieved_goal"],
state["desired_goal"])).reshape((1, -1)) - xm) / xs)
# print("predicted error", error.numpy(), err_avg.numpy())
# print("Tried ", tried, " initial states")
new_states, new_acts = man_controller.get_demo(env, state, render)
return new_states, new_acts
def get_experience(eps, env):
states, actions = [], []
for ep in range(eps):
state = env.reset()
new_states, new_acts = man_controller.get_demo(env, state)
states += new_states
actions += new_acts
return states, actions
def get_experience(eps, env, render = False):
states, actions = [], []
for ep in range(eps):
state = env.reset()
#state = robot_reset(env)
new_states, new_acts = man_controller.get_demo(env, state, CTRL_NORM, render)
states+=new_states
actions+=new_acts
return states, actions
def get_active_exp2(env, avg_error_trainset, model, ae, xm, xs, am, ast, render, take_max = False, max_act_steps = 20):
state = env.reset()
state = robot_reset(env)
succeded = True
while succeded:
state = env.reset()
state = robot_reset(env)
succeded, env, state, error = try_complete(model, ae, avg_error_trainset*ACTIVE_ERROR_THR, env, xm, xs, am, ast, render = RENDER_TEST)
#Here we have the env and the state where the robot doesn't know what to do.
time.sleep(1.)
# print("Expert demo.")
new_states, new_acts = man_controller.get_demo(env, state, CTRL_NORM, render)
# if len(new_states) > 100:
#If it's so long the demo failed.
#Should consider to reset it and try again, otherwise we waste a demo.
# return [], []
#Recursively call until a demo works.
while len(new_states) > 100:
#If it's so long the demo failed.
#Should consider to reset it and try again, otherwise we waste a demo.
new_states, new_acts = get_active_exp2(env, avg_error_trainset, model, ae, xm, xs, am, ast, render, take_max = False, max_act_steps = 20)
return new_states, new_acts