def run(args):
# logger
make_logger(args.result_dir)
# make envs
env = make_env(args)
# make config
config = make_config(args)
# make planner
planner = make_planner(args, config)
# make model
model = make_model(args, config)
# make controller
controller = make_controller(args, config, model)
# make simulator
runner = make_runner(args)
# run experiment
history_x, history_u, history_g = runner.run(env, controller, planner)
# plot results
plot_results(args, history_x, history_u, history_g=history_g)
save_plot_data(args, history_x, history_u, history_g=history_g)
def __init__(self, params):
self.params = params
self.env = make_env(params)
self.IL = ILNetwork(params)
self.collect_policy = MLPolicy(self.IL, params)
if self.params.env == "CartPole":
self.expert_policy = iLQRPolicy()
elif self.params.env == "TwoWheeledTrack":
self.expert_policy = NMPCCGMRESPolicy()
self.x_training_data = np.zeros((1, self.env.config['state_size']),
dtype=np.float32)
self.u_training_data = np.zeros((1, self.env.config['input_size']),
dtype=np.float32)
def run(args):
make_logger(args.result_dir)
env = make_env(args)
config = make_config(args)
planner = make_planner(args, config)
model = make_model(args, config)
controller = make_controller(args, config, model)
runner = make_runner(args)
history_x_all, history_u_all, history_g_all, history_cost_all = [], [], [], [
] # this is the collection list of n_sample trajectories
for iter_sample in range(args.n_sample):
print("Sampling {} th trajectory generated by expert policy:".format(
iter_sample))
history_x, history_u, history_g, cost = runner.run(
env, controller, planner)
history_x_all.append(history_x)
history_u_all.append(history_u)
history_g_all.append(history_g)
history_cost_all.append(cost)
plot_results(
history_x, history_u, history_g=history_g,
args=args) # no need to change now, just see the plot of the last traj
save_plot_data(history_x_all,
history_u_all,
history_g=history_g_all,
cost=history_cost_all,
args=args) # save lists
if args.save_anim:
animator = Animator(env, args=args)
animator.draw(history_x, history_g)
def __init__(self, params):
self.sess = tf.Session()
# build net and initialize variables when instantiate this class
self.env = make_env(params)
self.build_net()
self.sess.run(tf.global_variables_initializer())
def __init__(self, ILNet, params):
self.ILNet = ILNet
self.env = make_env(params)