from envs.WheeledRobotPybulletEnv import WheeledRobotPybulletEnv
from stable_baselines.ppo2.ppo2 import PPO2
from stable_baselines.common.vec_env import DummyVecEnv
import matplotlib.pyplot as plt
raw_env = WheeledRobotPybulletEnv(decision_interval=1,
use_GUI=True,
num_episode_steps=5)
# Optional: PPO2 requires a vectorized environment to run
# the env is now wrapped automatically when passing it to the constructor
vec_env = DummyVecEnv([lambda: raw_env])
dir_name = "results\LearningResults\PPO_WheeledRobotPybullet"
tensorboard_dir = dir_name + "\\tensorboard"
model_dir = dir_name + "\\model"
model = PPO2.load(model_dir, vec_env)
# model.learn(total_timesteps=100, tb_log_name="test")
# model.save(model_dir)
env = vec_env.envs[0]
obs_prev = env.reset()
x_poss = [env.snake_robot.x]
y_poss = [env.snake_robot.y]
thetas = [env.snake_robot.theta]
times = [0]
a1s = [env.snake_robot.a1]
a2s = [env.snake_robot.a2]
a1dots = [env.snake_robot.a1dot]
a2dots = [env.snake_robot.a2dot]
# robot_params = []
def load(self, path=None):
if path is None:
path = self.model_path
model = PPO2.load(path, env=self.env)
def run_ppo(config,
state_collector,
agent_name="ppo_99_8507750",
policy="CnnPolicy",
mode="train",
task_mode="static",
stack_offset=15,
num_stacks=1,
debug=True,
normalize=True,
disc_action_space=False,
ns=""):
path_to_models = config['PATHES']['path_to_models']
# Loading agent
model = PPO2.load("%s/%s/%s.pkl" %
(path_to_models, agent_name, agent_name))
print("Loaded %s" % agent_name)
print("--------------------------------------------------")
print("Normalize: ", normalize)
print("Policy: %s" % policy)
print("Discrete action space: ", disc_action_space)
print("Observation space size: ", model.observation_space.shape)
print("Debug: ", debug)
print("Number of stacks: %d, stack offset: %d" %
(model.observation_space.shape[2], stack_offset))
print("\n")
#Loading environment
env = load_train_env(ns, state_collector, 0.46, 19, num_stacks,
stack_offset, debug, task_mode, mode, policy,
disc_action_space, normalize)
# Resetting environment
if mode == "train" or mode == "eval":
obs = env.reset()
if mode == "exec" or mode == "exec_rw":
if disc_action_space:
obs, rewards, dones, info = env.step([5])
else:
obs, rewards, dones, info = env.step([[0.0, 0.0]])
if debug:
#Cummulative reward.
cum_reward = 0
while True:
#Determining action for given observation
action, _states = model.predict(obs)
# Clipping actions
if not disc_action_space:
action = np.maximum(np.minimum(model.action_space.high, action),
model.action_space.low)
#Executing action in environment
obs, rewards, dones, info = env.step(action)
if debug:
cum_reward += rewards
# Episode over?
if dones:
print("Episode finished with reward of %f." % cum_reward)
cum_reward = 0
time.sleep(0.0001)
if rospy.is_shutdown():
print('shutdown')
break
S = s_Dyymmdd_HHMM
"""
import datetime
date = str(datetime.datetime.now())
date = date[2:4] + date[5:7] + date[8:10] + '_' + date[11:13] + date[14:16] + date[17:19]
return s + '_D' + date
raw_env = IdealFluidSwimmerWithSpringEnv()
# Optional: PPO2 requires a vectorized environment to run
# the env is now wrapped automatically when passing it to the constructor
vec_env = DummyVecEnv([lambda: raw_env])
results_dir = '../results/PPO_IdealFluidSwimmerWithSpringFixedA1ddot/trial_1.22_D210122_163220'
model_path = os.path.join(results_dir, "model")
model = PPO2.load(model_path, vec_env)
env = vec_env.envs[0]
obs_prev = env.reset()
x_poss = [env.snake_robot.x]
y_poss = [env.snake_robot.y]
thetas = [env.snake_robot.theta]
times = [0]
a1s = [env.snake_robot.a1]
a2s = [env.snake_robot.a2]
a1dots = [env.snake_robot.a1dot]
a2dots = [env.snake_robot.a2dot]
a1ddots = [env.snake_robot.a1ddot]
ks = [env.snake_robot.k]
cs = [env.snake_robot.c]