params.test_episodes = 10
params.goal = 0
mu = str(params.mu).split(".")
mu = str(mu[0] + mu[1])
params.actor_model_dir = "../../logs/models/DDPG-original/{}/actor-mu{}/".format(
str(params.env_name.split("-")[0]), mu)
params.critic_model_dir = "../../logs/models/DDPG-original/{}/critic-mu{}/".format(
str(params.env_name.split("-")[0]), mu)
env = gym.make(params.env_name)
# env = Monitor(env, "./video/{}/".format(str(params.env_name.split("-")[0])), force=True)
tf.random.set_random_seed(params.seed)
random_process = None
agent = DDPG(Actor, Critic, env.action_space.shape[0], random_process, params)
# set seed
env.seed(params.seed)
state = env.reset()
done = False
r, episode_reward = list(), 0
reward_forward, reward_ctrl, reward_contact, reward_survive = list(), list(
), list(), list()
actions = list()
while not done:
# deterministic policy
action = agent.eval_predict(state)
# scale for execution in env (in DDPG, every action is clipped between [-1, 1] in agent.predict)
mu = str(params.mu).split(".")
mu = str(mu[0] + mu[1])
params.log_dir = "../../logs/logs/DDPG_batchnorm-{}-seed{}/{}-mu{}".format(
params.train_flg, params.seed, str(params.env_name.split("-")[0]), mu)
params.actor_model_dir = "../../logs/models/DDPG_batchnorm-{}-seed{}/{}/actor-mu{}/".format(
params.train_flg, params.seed, str(params.env_name.split("-")[0]), mu)
params.critic_model_dir = "../../logs/models/DDPG_batchnorm-{}-seed{}/{}/critic-mu{}/".format(
params.train_flg, params.seed, str(params.env_name.split("-")[0]), mu)
params.video_dir = "../../logs/video/DDPG_batchnorm-{}-seed{}/{}-mu{}/".format(
params.train_flg, params.seed, str(params.env_name.split("-")[0]), mu)
params.plot_path = "../../logs/plots/DDPG_batchnorm-{}-seed{}/{}-mu{}/".format(
params.train_flg, params.seed, str(params.env_name.split("-")[0]), mu)
env = gym.make(params.env_name)
env = Monitor(env, params.video_dir)
# set seed
env.seed(params.seed)
tf.random.set_random_seed(params.seed)
replay_buffer = ReplayBuffer(params.memory_size)
reward_buffer = deque(maxlen=params.reward_buffer_ep)
summary_writer = tf.contrib.summary.create_file_writer(params.log_dir)
random_process = OrnsteinUhlenbeckProcess(size=env.action_space.shape[0],
theta=0.15,
mu=params.mu,
sigma=params.sigma)
# random_process = GaussianNoise(mu=params.mu, sigma=params.sigma)
agent = DDPG(Actor, Critic, env.action_space.shape[0], random_process, params)
train_DDPG_original(agent, env, replay_buffer, reward_buffer, summary_writer)
now = datetime.datetime.now()
if params.debug_flg:
params.log_dir = "../logs/logs/" + now.strftime("%Y%m%d-%H%M%S") + "-DDPG/"
params.model_dir = "../logs/models/" + now.strftime("%Y%m%d-%H%M%S") + "-DDPG/"
else:
params.log_dir = "../logs/logs/{}".format(params.env_name)
params.model_dir = "../logs/models/{}".format(params.env_name)
env = gym.make(params.env_name)
# set seed
env.seed(params.seed)
tf.compat.v1.random.set_random_seed(params.seed)
agent = DDPG(Actor, Critic, env.action_space.shape[0], params)
replay_buffer = ReplayBuffer(params.memory_size)
reward_buffer = deque(maxlen=params.reward_buffer_ep)
summary_writer = tf.contrib.summary.create_file_writer(params.log_dir)
init_state = env.reset() # reset
agent.predict(init_state) # burn the format of the input matrix to get the weight matrices!!
gp_model, update = create_bayes_net()
optimiser = tf.compat.v1.train.AdamOptimizer()
num_sample = 100 # number of sampling
get_ready(agent.params)
global_timestep = tf.compat.v1.train.get_or_create_global_step()
time_buffer = deque(maxlen=agent.params.reward_buffer_ep)
log = logger(agent.params)
params.plot_path = "./logs/plots/DDPG-seed{}/".format(params.seed)
env = make_grid_env(plot_path=params.plot_path)
# set seed
env.seed(params.seed)
tf.random.set_random_seed(params.seed)
replay_buffer = ReplayBuffer(params.memory_size)
reward_buffer = deque(maxlen=params.reward_buffer_ep)
summary_writer = tf.contrib.summary.create_file_writer(params.log_dir)
random_process = OrnsteinUhlenbeckProcess(size=env.action_space.shape[0],
theta=0.15,
mu=params.mu,
sigma=params.sigma)
agent = DDPG(Actor, Critic, env.action_space.shape[0], random_process, params)
get_ready(agent.params)
global_timestep = tf.compat.v1.train.get_or_create_global_step()
time_buffer = deque(maxlen=agent.params.reward_buffer_ep)
log = logger(agent.params)
traj = list()
with summary_writer.as_default():
# for summary purpose, we put all codes in this context
with tf.contrib.summary.always_record_summaries():
for i in itertools.count():
state = env.reset()
now = datetime.now()
params.log_dir += "{}".format(params.env_name)
params.model_dir += "{}".format(params.env_name)
rospy.init_node("start_her")
task_and_robot_environment_name = rospy.get_param(
'/fetch/task_and_robot_environment_name')
# to register our task env to openai env.
# so that we don't care the output of this method for now.
env = StartOpenAI_ROS_Environment(task_and_robot_environment_name)
# params.max_action = env.action_space.high[0]
# params.num_action = env.action_space.shape[0]
# TODO: this is temp solution..... check openai's fetch's implementation!!
params.max_action = 0
params.num_action = 4
# set seed
env.seed(params.seed)
tf.random.set_random_seed(params.seed)
agent = DDPG(Actor, Critic, params.num_action, params)
replay_buffer = ReplayBuffer(params.memory_size)
reward_buffer = deque(maxlen=params.num_episodes)
summary_writer = tf.contrib.summary.create_file_writer(params.log_dir)
train_HER(agent, env, replay_buffer, reward_buffer, summary_writer)
help="debug mode or not")
parser.add_argument("--google_colab",
default=False,
type=bool,
help="if you are executing this on GoogleColab")
params = parser.parse_args()
params.test_episodes = 10
params.goal = DDPG_ENV_LIST[params.env_name]
now = datetime.now()
if params.debug_flg:
params.log_dir = "../../logs/logs/" + now.strftime(
"%Y%m%d-%H%M%S") + "-DDPG/"
params.model_dir = "../../logs/models/" + now.strftime(
"%Y%m%d-%H%M%S") + "-DDPG/"
else:
params.log_dir = "../../logs/logs/{}".format(params.env_name)
params.model_dir = "../../logs/models/{}".format(params.env_name)
env = gym.make(params.env_name)
# set seed
env.seed(params.seed)
tf.random.set_random_seed(params.seed)
agent = DDPG(Actor, Critic, env.action_space.shape[0], params)
replay_buffer = ReplayBuffer(params.memory_size)
reward_buffer = deque(maxlen=params.reward_buffer_ep)
summary_writer = tf.contrib.summary.create_file_writer(params.log_dir)
train_DDPG(agent, env, replay_buffer, reward_buffer, summary_writer)
