def execute(env, init_state, steps, init_mean, init_var, model, config,
last_action_seq, task_likelihoods, pred_high, pred_low, recorder):
# current_state = env.reset()
current_state = copy.copy(env.state) if config['online'] else env.reset()
try:
config["goal"] = env.goal
except:
pass
trajectory = []
traject_cost = 0
sliding_mean = init_mean # np.zeros(config["sol_dim"])
temp_config = copy.deepcopy(config)
temp_config["popsize"] = 20000
optimizer = None
sol = None
bar = ProgBar(steps,
track_time=True,
title='\nExecuting....',
bar_char='▒')
for i in range(steps):
cost_object = Cost(model=model,
init_state=current_state,
horizon=config["horizon"],
task_likelihoods=task_likelihoods,
action_dim=env.action_space.shape[0],
goal=config["goal"],
pred_high=pred_high,
pred_low=pred_low)
config["cost_fn"] = cost_object.cost_fn
optimizer = RS_opt(config)
# sol = optimizer.obtain_solution(sliding_mean, init_var)
sol = optimizer.obtain_solution()
a = sol[0:env.action_space.shape[0]]
next_state, r = 0, 0
for k in range(1):
if config["record_video"]:
recorder.capture_frame()
next_state, r, _, _ = env.step(a)
# env.joint_reset()
trajectory.append(
[current_state.copy(),
a.copy(), next_state - current_state, -r])
current_state = next_state
traject_cost += -r
# sliding_mean = last_action_seq[i*config["sol_dim"] : (i+1) * config["sol_dim"]]
# sliding_mean[0:-len(a)] = sol[len(a)::]
# sliding_mean[-len(a)::] = sol[-len(a)::]
bar.update(item_id=" Step " + str(i) + " ")
if config["record_video"]:
recorder.capture_frame()
recorder.close()
return trajectory, traject_cost
def execute_random(env, steps, init_state):
current_state = env.reset()
trajectory = []
traject_cost = 0
for i in range(steps):
a = env.action_space.sample()
next_state, r = 0, 0
for k in range(1):
next_state, r, _, _ = env.step(a)
trajectory.append(
[current_state.copy(),
a.copy(), next_state - current_state, -r])
current_state = next_state
traject_cost += -r
return np.array(trajectory), traject_cost
def execute(env, init_state, steps, init_mean, init_var, model, config,
last_action_seq, pred_high, pred_low):
current_state = env.reset()
try:
config["goal"] = env.goal
except:
pass
trajectory = []
traject_cost = 0
model_error = 0
sliding_mean = np.zeros(config["sol_dim"])
rand = np.random.rand(config["sol_dim"])
bar = ProgBar(steps,
track_time=True,
title='\nExecuting....',
bar_char='▒')
for i in range(steps):
cost_object = Cost(ensemble_model=model,
init_state=current_state,
horizon=config["horizon"],
action_dim=env.action_space.shape[0],
goal=config["goal"],
pred_high=pred_high,
pred_low=pred_low)
config["cost_fn"] = cost_object.cost_fn
optimizer = RS_opt(config)
sol = optimizer.obtain_solution()
## Take soft action
a = sol[0:env.action_space.shape[
0]] #if i == 0 else sol[0:env.action_space.shape[0]] * 0.8 + a * 0.2
next_state, r = 0, 0
for k in range(1):
next_state, r, _, _ = env.step(a)
trajectory.append(
[current_state.copy(),
a.copy(), next_state - current_state, -r])
model_error += test_model(model, current_state.copy(), a.copy(),
next_state - current_state)
current_state = next_state
traject_cost += -r
sliding_mean[0:-len(a)] = sol[len(a)::]
bar.update(item_id=" Step " + str(i) + " ")
print("Model error: ", model_error)
return np.array(trajectory), traject_cost