def launch(args):
# create the ddpg_agent
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)
# env = gym.make(args.env_name)
# set random seeds for reproduce
env.seed(args.seed + MPI.COMM_WORLD.Get_rank())
random.seed(args.seed + MPI.COMM_WORLD.Get_rank())
np.random.seed(args.seed + MPI.COMM_WORLD.Get_rank())
torch.manual_seed(args.seed + MPI.COMM_WORLD.Get_rank())
if args.cuda:
torch.cuda.manual_seed(args.seed + MPI.COMM_WORLD.Get_rank())
# get the environment parameters
env_params = get_env_params(env)
# create the ddpg agent to interact with the environment
ddpg_trainer = ddpg_agent(args, env, env_params)
ddpg_trainer.learn()
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)
nsteps = rospy.get_param("/turtlebot2/nsteps")
running_step = rospy.get_param("/turtlebot2/running_step")
"""
# Hyperparameters
gamma = 0.79 # initially 0.99 discount factor
seed = 543 # random seed
n_epochs = 20 # number of epochs to test the trained model
test_global_step = 0 # Global number of testing steps for tracking cummulative rewards in Tensorboard
# If gpu is to be used
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Fix random seed (for reproducibility)
env.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
# Get number of actions from gym action space
#n_inputs = env.observation_space.shape[0]
n_inputs = 5
n_actions = env.action_space.n
policy_net = DQN(n_inputs, n_actions).to(device)
policy_net.load_state_dict(torch.load(MODEL_PATH, map_location=device))
policy_net.eval()
####################################################################################################################
#logdir = os.path.join("$HOME/python3_ws/src/turtle2_openai_ros_example/src/logs", datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
basedir = os.path.dirname(__file__)
