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LESSON
Commit 50ae1f7d by Gaoyunkai
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add goal correct

parent 6ed87ec7
Showing with 1018 additions and 9 deletions
README.md
......@@ -8,5 +8,9 @@ The python dependencies are as follows.
* [Gym](https://gym.openai.com/)
* [Mujoco](https://www.roboti.us)
Run the codes with ``python train_hier_sac.py``. The tensorboard files are saved in the ``runs`` folder and the
trained models are saved in the ``saved_models`` folder.
The tensorboard files are saved in ``/lustre/S/gaoyunkai/RL/LESSON/runs/hier/`` folder
the trained models are saved in the ``save-dir`` of arguments_hier_sac.py folder.
Run the origin codes with ``python train_hier_sac.py``.
Run code of the high agent and low agent that use SAC algorithm with ``python train_hier_double_sac.py``
Run code of double_SAC and goal_correct with ``python train_hier_double_sac_goal_correct.py``
\ No newline at end of file
algos/hier_double_sac_goal_correct.py 0 → 100644
import os
import sys
sys.path.append('../')
from datetime import datetime
from tensorboardX import SummaryWriter
from models.networks import *
from algos.replay_buffer import replay_buffer, replay_buffer_energy
from algos.her import her_sampler
# from planner.goal_plan import *
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import time
from algos.sac.sac import SAC
from algos.sac.model import GaussianPolicy
from algos.sac.replay_memory import ReplayMemory, Array_ReplayMemory
import gym
import pickle
# from planner.simhash import HashingBonusEvaluator
from PIL import Image
import imageio
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
sns.set_color_codes()
SUBGOAL_RANGE = 200.0
class hier_sac_agent:
def __init__(self, args, env, env_params, test_env, test_env1=None, test_env2=None):
self.args = args
self.env = env
self.test_env = test_env
self.env_params = env_params
self.device = args.device
self.resume = args.resume
self.resume_epoch = args.resume_epoch
self.not_train_low = False
self.test_env1 = test_env1
self.test_env2 = test_env2
self.old_sample = args.old_sample
self.low_dim = env_params['obs']
self.env_params['low_dim'] = self.low_dim
self.hi_dim = env_params['obs']
print("hi_dim", self.hi_dim)
self.learn_goal_space = True
self.whole_obs = False # use whole observation space as subgoal space
self.abs_range = abs_range = args.abs_range # absolute goal range
self.feature_reg = 0.0 # feature l2 regularization
print("abs_range", abs_range)
if args.env_name[:5] == "Fetch":
maze_low = self.env.env.initial_gripper_xpos[:2] - self.env.env.target_range
maze_high = self.env.env.initial_gripper_xpos[:2] + self.env.env.target_range
self.hi_act_space = gym.spaces.Box(low=maze_low, high=maze_high)
else:
if args.env_name != "NChain-v1":
self.hi_act_space = self.env.env.maze_space
else:
self.hi_act_space = gym.spaces.Box(low=np.array([-1]), high=np.array([1]))
if self.learn_goal_space:
if args.env_name == "NChain-v1":
self.hi_act_space = gym.spaces.Box(low=np.array([-abs_range]), high=np.array([abs_range]))
else:
self.hi_act_space = gym.spaces.Box(low=np.array([-abs_range, -abs_range]), high=np.array([abs_range, abs_range])) # goal_dim=2
#self.hi_act_space = gym.spaces.Box(low=-np.ones(4)*abs_range, high=np.ones(4)*abs_range) #goal_dim=4
if self.whole_obs:
vel_low = [-10.] * 4
vel_high = [10.] * 4
maze_low = np.concatenate((self.env.env.maze_low, np.array(vel_low)))
maze_high = np.concatenate((self.env.env.maze_high, np.array(vel_high)))
self.hi_act_space = gym.spaces.Box(low=maze_low, high=maze_high)
dense_low = True
self.low_use_clip = not dense_low # only sparse reward use clip
if args.replay_strategy == "future":
self.low_forward = True
assert self.low_use_clip is True
else:
self.low_forward = False
assert self.low_use_clip is False
self.hi_sparse = (self.env.env.reward_type == "sparse")
# # params of learning phi
resume_phi = args.resume
self.not_update_phi = False
phi_path = args.resume_path
# resume_phi = True
# phi_path = 'saved_models/AntMaze1-v1_Jun01_19-26-19'
# self.not_update_phi = True
self.save_fig = False
self.save_model = False
self.start_update_phi = args.start_update_phi
self.early_stop = args.early_stop # after success rate converge, don't update low policy and feature
if args.env_name in ['AntPush-v1', 'AntFall-v1']:
if self.not_update_phi:
self.early_stop_thres = 900
else:
self.early_stop_thres = 3500
elif args.env_name in ["PointMaze1-v1"]:
self.early_stop_thres = 2000
elif args.env_name == "AntMaze1-v1":
self.early_stop_thres = 3000
else:
self.early_stop_thres = args.n_epochs
print("early_stop_threshold", self.early_stop_thres)
self.success_log = []
# scaling = self.env.env.env.MAZE_SIZE_SCALING
# print("scaling", scaling)
self.count_latent = False
if self.count_latent:
self.hash = HashingBonusEvaluator(512, 2)
self.count_obs = False
if self.count_obs:
self.hash = HashingBonusEvaluator(512, env_params['obs'])
self.high_correct = False
self.k = args.c
self.delta_k = 0
self.prediction_coeff = 0.0
tanh_output = False
self.use_prob = False
print("prediction_coeff", self.prediction_coeff)
if args.save:
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
self.log_dir = '/lustre/S/gaoyunkai/RL/LESSON/runs/hier/' + str(args.env_name) + '/RB_Decay_' + current_time + \
"_C_" + str(args.c) + "_Image_" + str(args.image) + \
"_Seed_" + str(args.seed) + "_Reward_" + str(args.low_reward_coeff) + \
"_NoPhi_" + str(self.not_update_phi) + "_LearnG_" + str(self.learn_goal_space) + "_Early_" + str(self.early_stop_thres) + str(args.early_stop)
self.writer = SummaryWriter(log_dir=self.log_dir)
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
# path to save the model
self.model_path = os.path.join(self.args.save_dir, self.args.env_name + "_" + current_time)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
# init low-level network
self.real_goal_dim = self.hi_act_space.shape[0] # low-level goal space and high-level action space
self.env_params['real_goal_dim'] = self.real_goal_dim
self.low_act_space = gym.spaces.Box(low=np.ones(self.env_params["action"]) * -self.env_params["action_max"],
high=np.ones(self.env_params["action"]) * self.env_params["action_max"])
self.init_network()
# init high-level agent
self.hi_agent = SAC(self.hi_dim + env_params['goal'], self.hi_act_space, args, False, env_params['goal'],
args.gradient_flow_value, args.abs_range, tanh_output, use_goal_correct=True)
self.hi_buffer = ReplayMemory(args.buffer_size, use_goal_correct=True)
# her sampler
self.c = self.args.c # interval of high level action
self.low_her_module = her_sampler(args.replay_strategy, args.replay_k, args.distance, args.future_step,
dense_reward=dense_low, direction_reward=False, low_reward_coeff=args.low_reward_coeff)
if args.env_name[:5] == "Fetch":
self.low_buffer = replay_buffer_energy(self.env_params, self.args.buffer_size,
self.low_her_module.sample_her_energy, args.env_name)
else:
self.low_buffer = replay_buffer(self.env_params, self.args.buffer_size, self.low_her_module.sample_her_transitions)
not_load_buffer, not_load_high = True, False
if self.resume is True:
self.start_epoch = self.resume_epoch
if not not_load_high:
self.hi_agent.policy.load_state_dict(torch.load(self.args.resume_path + \
'/hi_actor_model.pt', map_location='cuda:4')[0])
# self.hi_agent.critic.load_state_dict(torch.load(self.args.resume_path + \
# '/hi_critic_model.pt', map_location='cuda:4')[0])
# print("not load low !!!")
print("load low !!!")
self.low_actor_network.load_state_dict(torch.load(self.args.resume_path + \
'/low_actor_model.pt', map_location='cuda:4')[0])
self.low_critic_network.load_state_dict(torch.load(self.args.resume_path + \
'/low_critic_model.pt', map_location='cuda:4')[0])
if not not_load_buffer:
# self.hi_buffer = torch.load(self.args.resume_path + '/hi_buffer.pt', map_location='cuda:1')
self.low_buffer = torch.load(self.args.resume_path + '/low_buffer.pt', map_location='cuda:1')
# sync target network of low-level
self.sync_target()
if hasattr(self.env.env, 'env'):
self.animate = self.env.env.env.visualize_goal
else:
self.animate = self.args.animate
self.distance_threshold = self.args.distance
if not (args.gradient_flow or args.use_prediction or args.gradient_flow_value):
self.representation = RepresentationNetwork(env_params, 3, self.abs_range, self.real_goal_dim).to(args.device)
if args.use_target:
self.target_phi = RepresentationNetwork(env_params, 3, self.abs_range, 2).to(args.device)
# load the weights into the target networks
self.target_phi.load_state_dict(self.representation.state_dict())
self.representation_optim = torch.optim.Adam(self.representation.parameters(), lr=0.0001)
if resume_phi is True:
print("load phi from: ", phi_path)
self.representation.load_state_dict(torch.load(phi_path + \
'/phi_model_4000.pt', map_location='cuda:4')[0])
elif args.use_prediction:
self.representation = DynamicsNetwork(env_params, self.abs_range, 2, tanh_output=tanh_output, use_prob=self.use_prob, device=args.device).to(args.device)
self.representation_optim = torch.optim.Adam(self.representation.parameters(), lr=0.0001)
if resume_phi is True:
print("load phi from: ", phi_path)
self.representation.load_state_dict(torch.load(phi_path + \
'/phi_model_4000.pt', map_location='cuda:1')[0])
print("learn goal space", self.learn_goal_space, " update phi", not self.not_update_phi)
self.train_success = 0
self.furthest_task = 0.
print("env_params:", env_params)
def adjust_lr_actor(self, epoch):
lr_actor = self.args.lr_actor * (0.5 ** (epoch // self.args.lr_decay_actor))
for param_group in self.low_actor_optim.param_groups:
param_group['lr'] = lr_actor
def adjust_lr_critic(self, epoch):
lr_critic = self.args.lr_critic * (0.5 ** (epoch // self.args.lr_decay_critic))
for param_group in self.low_critic_optim.param_groups:
param_group['lr'] = lr_critic
def learn(self):
for epoch in range(self.start_epoch, self.args.n_epochs):
if epoch > 0 and epoch % self.args.lr_decay_actor == 0:
self.adjust_lr_actor(epoch)
if epoch > 0 and epoch % self.args.lr_decay_critic == 0:
self.adjust_lr_critic(epoch)
ep_obs, ep_ag, ep_g, ep_actions = [], [], [], []
last_hi_obs = None
success = 0
observation = self.env.reset()
obs = observation['observation']
ag = observation['achieved_goal'][:self.real_goal_dim]
g = observation['desired_goal']
# identify furthest task
if g[1] >= 8:
self.furthest_task += 1
is_furthest_task = True
else:
is_furthest_task = False
if self.learn_goal_space:
if self.args.gradient_flow:
if self.args.use_target:
ag = self.hi_agent.policy_target.phi(torch.Tensor(obs).to(self.device)).detach().cpu().numpy()
else:
ag = self.hi_agent.policy.phi(torch.Tensor(obs).to(self.device)).detach().cpu().numpy()
elif self.args.gradient_flow_value:
ag = self.hi_agent.critic.phi(torch.Tensor(obs).to(self.device)).detach().cpu().numpy()[0]
elif self.args.use_prediction:
ag = self.representation.phi(torch.Tensor(obs).to(self.device)).detach().cpu().numpy()[0]
else:
if self.args.use_target:
ag = self.target_phi(torch.Tensor(obs).to(self.device)).detach().cpu().numpy()[0]
else:
ag = self.representation(torch.Tensor(obs).to(self.device)).detach().cpu().numpy()[0]
if self.whole_obs:
ag = obs.copy()
low_action_c_step = []
low_state_c_step = []
for t in range(self.env_params['max_timesteps']):
act_obs, act_g = self._preproc_inputs(obs, g)
if t % self.c == 0:
hi_act_obs = np.concatenate((obs[:self.hi_dim], g))
# append high-level rollouts
if last_hi_obs is not None:
mask = float(not done)
if self.high_correct:
last_hi_a = ag
self.hi_buffer.push(last_hi_obs, last_hi_a, last_hi_r, hi_act_obs, mask, epoch, low_state_c_step, low_action_c_step)
low_action_c_step = []
low_state_c_step = []
if epoch < self.args.start_epoch:
hi_action = self.hi_act_space.sample()
# print("sample", hi_action)
else:
hi_action = self.hi_agent.select_action(hi_act_obs)
last_hi_obs = hi_act_obs.copy()
last_hi_a = hi_action.copy()
last_hi_r = 0.
done = False
if self.old_sample:
hi_action_for_low = hi_action
else:
# make hi_action a delta phi(s)
hi_action_for_low = ag.copy() + hi_action.copy()
hi_action_for_low = np.clip(hi_action_for_low, -SUBGOAL_RANGE, SUBGOAL_RANGE)
hi_action_tensor = torch.tensor(hi_action_for_low, dtype=torch.float32).unsqueeze(0).to(self.device)
# update high-level policy
if len(self.hi_buffer) > self.args.batch_size:
self.update_hi(epoch)
with torch.no_grad():
if self.not_train_low:
action = self.test_policy(act_obs[:, :self.low_dim], hi_action_tensor)
else:
action = self.explore_policy(act_obs[:, :self.low_dim], hi_action_tensor)
low_action_c_step.append(action.copy())
low_state_c_step.append(obs[:self.low_dim].copy())
# feed the actions into the environment
observation_new, r, _, info = self.env.step(action)
if info['is_success']:
done = True
# only record the first success
if success == 0 and is_furthest_task:
success = t
self.train_success += 1
if self.animate:
self.env.render()
obs_new = observation_new['observation']
ag_new = observation_new['achieved_goal'][:self.real_goal_dim]
if self.learn_goal_space:
if self.args.gradient_flow:
if self.args.use_target:
ag_new = self.hi_agent.policy_target.phi(
torch.Tensor(obs_new).to(self.device)).detach().cpu().numpy()
else:
ag_new = self.hi_agent.policy.phi(torch.Tensor(obs_new).to(self.device)).detach().cpu().numpy()
elif self.args.gradient_flow_value:
ag_new = self.hi_agent.critic.phi(torch.Tensor(obs_new).to(self.device)).detach().cpu().numpy()[0]
elif self.args.use_prediction:
ag_new = self.representation.phi(torch.Tensor(obs_new).to(self.device)).detach().cpu().numpy()[0]
else:
if self.args.use_target:
ag_new = self.target_phi(torch.Tensor(obs_new).to(self.device)).detach().cpu().numpy()[0]
else:
ag_new = self.representation(torch.Tensor(obs_new).to(self.device)).detach().cpu().numpy()[0]
if self.whole_obs:
ag_new = obs_new.copy()
if done is False:
if self.count_latent:
self.hash.inc_hash(ag[None])
r += self.hash.predict(ag_new[None])[0] * 0.1
if self.count_obs:
self.hash.inc_hash(obs[None])
r += self.hash.predict(obs_new[None])[0] * 0.1
last_hi_r += r
# append rollouts
ep_obs.append(obs[:self.low_dim].copy())
ep_ag.append(ag.copy())
ep_g.append(hi_action_for_low.copy())
ep_actions.append(action.copy())
# re-assign the observation
obs = obs_new
ag = ag_new
# slowly update phi
if epoch > self.start_update_phi and not self.not_update_phi and not self.args.gradient_flow and not self.args.gradient_flow_value:
self.slow_update_phi(epoch)
if t % self.args.period == 0 and self.args.use_target:
self._soft_update_target_network(self.target_phi, self.representation)
ep_obs.append(obs[:self.low_dim].copy())
ep_ag.append(ag.copy())
mask = float(not done)
hi_act_obs = np.concatenate((obs[:self.hi_dim], g))
self.hi_buffer.push(last_hi_obs, last_hi_a, last_hi_r, hi_act_obs, mask, epoch, low_state_c_step, low_action_c_step)
mb_obs = np.array([ep_obs])
mb_ag = np.array([ep_ag])
mb_g = np.array([ep_g])
mb_actions = np.array([ep_actions])
self.low_buffer.store_episode([mb_obs, mb_ag, mb_g, mb_actions, success, False])
if self.args.save and self.args.env_name == "NChain-v1":
self.writer.add_scalar('Explore/coverage_' + self.args.env_name, self.env.env.coverage, epoch)
# print("coverage", self.env.env.coverage)
# update low-level
if not self.not_train_low:
for n_batch in range(self.args.n_batches):
self._update_network(epoch, self.low_buffer, self.low_actor_target_network,
self.low_critic_target_network,
self.low_actor_network, self.low_critic_network, 'max_timesteps',
self.low_actor_optim, self.low_critic_optim, use_forward_loss=self.low_forward, clip=self.low_use_clip)
if n_batch % self.args.period == 0:
self._soft_update_target_network(self.low_actor_target_network, self.low_actor_network)
self._soft_update_target_network(self.low_critic_target_network, self.low_critic_network)
# start to do the evaluation
if epoch % self.args.eval_interval == 0 and epoch != 0:
if self.test_env1 is not None:
eval_success1, _ = self._eval_hier_agent(env=self.test_env1)
eval_success2, _ = self._eval_hier_agent(env=self.test_env2)
farthest_success_rate, _ = self._eval_hier_agent(env=self.test_env)
random_success_rate, _ = self._eval_hier_agent(env=self.env)
self.success_log.append(farthest_success_rate)
mean_success = np.mean(self.success_log[-5:])
# stop updating phi and low
if self.early_stop and (mean_success >= 0.9 or epoch > self.early_stop_thres):
print("early stop !!!")
self.not_update_phi = True
self.not_train_low = True
print('[{}] epoch is: {}, eval hier success rate is: {:.3f}'.format(datetime.now(), epoch, random_success_rate))
if self.save_fig:
self.vis_hier_policy(epoch=epoch)
self.visualize_representation(epoch=epoch)
if self.args.save:
print("log_dir: ", self.log_dir)
torch.save([self.hi_agent.critic.state_dict()], self.model_path + '/hi_critic_model.pt')
torch.save([self.low_critic_network.state_dict()], self.model_path + '/low_critic_model.pt')
torch.save(self.hi_buffer, self.model_path + '/hi_buffer.pt')
torch.save(self.low_buffer, self.model_path + '/low_buffer.pt')
if not self.args.gradient_flow and not self.args.gradient_flow_value:
if self.save_model:
# self.cal_MIV(epoch)
torch.save([self.representation.state_dict()], self.model_path + '/phi_model_{}.pt'.format(epoch))
torch.save([self.hi_agent.policy.state_dict()], self.model_path + '/hi_actor_{}.pt'.format(epoch))
torch.save([self.low_actor_network.state_dict()], self.model_path + '/low_actor_{}.pt'.format(epoch))
else:
torch.save([self.representation.state_dict()], self.model_path + '/phi_model.pt')
torch.save([self.hi_agent.policy.state_dict()], self.model_path + '/hi_actor_model.pt')
torch.save([self.low_actor_network.state_dict()], self.model_path + '/low_actor_model.pt')
self.writer.add_scalar('Success_rate/hier_farthest_' + self.args.env_name, farthest_success_rate, epoch)
self.writer.add_scalar('Success_rate/hier_random_' + self.args.env_name, random_success_rate, epoch)
self.writer.add_scalar('Explore/furthest_task_' + self.args.env_name, self.furthest_task, epoch)
if self.test_env1 is not None:
self.writer.add_scalar('Success_rate/eval1_' + self.args.env_name,
eval_success1, epoch)
self.writer.add_scalar('Success_rate/eval2_' + self.args.env_name, eval_success2,
epoch)
# pre_process the inputs
def _preproc_inputs(self, obs, g):
obs = torch.tensor(obs, dtype=torch.float32).unsqueeze(0).to(self.device)
g = torch.tensor(g, dtype=torch.float32).unsqueeze(0).to(self.device)
return obs, g
# this function will choose action for the agent and do the exploration
def _select_actions(self, pi):
action = pi.cpu().numpy().squeeze()
if action.shape == ():
action = np.array([action])
# add the gaussian
action += self.args.noise_eps * self.env_params['action_max'] * np.random.randn(*action.shape)
action = np.clip(action, -self.env_params['action_max'], self.env_params['action_max'])
# random actions...
if np.random.rand() < self.args.random_eps:
action = np.random.uniform(low=-self.env_params['action_max'], high=self.env_params['action_max'], \
size=self.env_params['action'])
return action
def explore_policy(self, obs, goal):
state = torch.cat([obs, goal], dim=1)
pi, _, _ = self.low_actor_network.sample(state)
action = pi.cpu().numpy().squeeze()
return action
def update_hi(self, epoch):
if self.args.gradient_flow or self.args.gradient_flow_value:
sample_data, _ = self.slow_collect()
sample_data = torch.tensor(sample_data, dtype=torch.float32).to(self.device)
else:
sample_data = None
critic_1_loss, critic_2_loss, policy_loss, _, _ = self.hi_agent.update_parameters(self.hi_buffer,
self.args.batch_size,
self.env_params,
self.hi_sparse,
sample_data,
self.low_actor_network,
self.representation)
if self.args.save:
self.writer.add_scalar('Loss/hi_critic_1', critic_1_loss, epoch)
self.writer.add_scalar('Loss/hi_critic_2', critic_2_loss, epoch)
self.writer.add_scalar('Loss/hi_policy', policy_loss, epoch)
def random_policy(self, obs, goal):
random_actions = np.random.uniform(low=-self.env_params['action_max'], high=self.env_params['action_max'], \
size=self.env_params['action'])
return random_actions
def test_policy(self, obs, goal):
state = torch.cat([obs, goal], dim=1)
pi, _, _ = self.low_actor_network.sample(state)
# convert the actions
actions = pi.detach().cpu().numpy().squeeze()
if actions.shape == ():
actions = np.array([actions])
return actions
# soft update
def _soft_update_target_network(self, target, source):
for target_param, param in zip(target.parameters(), source.parameters()):
target_param.data.copy_((1 - self.args.polyak) * param.data + self.args.polyak * target_param.data)
# update the network
def _update_network(self, epoch, buffer, actor_target, critic_target, actor, critic, T, actor_optim, critic_optim, use_forward_loss=True, clip=True):
# sample the episodes
transitions = buffer.sample(self.args.batch_size)
# pre-process the observation and goal
o, o_next, g, ag = transitions['obs'], transitions['obs_next'], transitions['g'], transitions['ag']
transitions['obs'], transitions['g'] = o, g
transitions['obs_next'], transitions['g_next'] = o_next, g
ag_next = transitions['ag_next']
# start to do the update
obs_cur = transitions['obs']
g_cur = transitions['g']
obs_next = transitions['obs_next']
g_next = transitions['g_next']
# done
dist = np.linalg.norm(ag_next - g_next, axis=1)
not_done = (dist > self.distance_threshold).astype(np.int32).reshape(-1, 1)
# transfer them into the tensor
obs_cur = torch.tensor(obs_cur, dtype=torch.float32).to(self.device)
g_cur = torch.tensor(g_cur, dtype=torch.float32).to(self.device)
obs_next = torch.tensor(obs_next, dtype=torch.float32).to(self.device)
g_next = torch.tensor(g_next, dtype=torch.float32).to(self.device)
ag_next = torch.tensor(ag_next, dtype=torch.float32).to(self.device)
not_done = torch.tensor(not_done, dtype=torch.int32).to(self.device)
actions_tensor = torch.tensor(transitions['actions'], dtype=torch.float32).to(self.device)
r_tensor = torch.tensor(transitions['r'], dtype=torch.float32).to(self.device)
# calculate the target Q value function
with torch.no_grad():
state_next = torch.cat([obs_next, g_next], dim=1)
actions_next, actions_next_log, _ = actor_target.sample(state_next)
q1_next_value, q2_next_value = critic_target(obs_next, g_next, actions_next)
min_q_next_value = torch.min(q1_next_value, q2_next_value) - self.args.alpha * actions_next_log
target_q_value = r_tensor + critic_target.gamma * min_q_next_value * not_done
if clip:
clip_return = self.env_params[T]
target_q_value = torch.clamp(target_q_value, -clip_return, 0.)
# the q loss
q1_value, q2_value= critic(obs_cur, g_cur, actions_tensor)
q1_loss = F.mse_loss(q1_value, target_q_value)
q2_loss = F.mse_loss(q2_value, target_q_value)
critic_loss = q1_loss + q2_loss
critic_optim.zero_grad()
critic_loss.backward()
torch.nn.utils.clip_grad_norm_(self.low_critic_network.parameters(), 1.0)
critic_optim.step()
# the actor loss
state = torch.cat([obs_cur, g_cur], dim=1)
actions_real, actions_real_log, _ = actor.sample(state)
q1_new, q2_new = critic(obs_cur, g_cur, actions_real)
min_q_new = torch.min(q1_new, q2_new)
actor_loss = ((self.args.alpha * actions_real_log) - min_q_new).mean()
# start to update the network
actor_optim.zero_grad()
actor_loss.backward()
torch.nn.utils.clip_grad_norm_(self.low_actor_network.parameters(), 1.0)
actor_optim.step()
# update the critic_network
if self.args.save:
if T == 'max_timesteps':
name = 'low'
else:
name = 'high'
self.writer.add_scalar('Loss/' + name + '_actor_loss' + self.args.metric, actor_loss, epoch)
self.writer.add_scalar('Loss/' + name + '_critic_loss' + self.args.metric, critic_loss, epoch)
def _eval_hier_agent(self, env, n_test_rollouts=10):
total_success_rate = []
if not self.args.eval:
n_test_rollouts = self.args.n_test_rollouts
discount_reward = np.zeros(n_test_rollouts)
for roll in range(n_test_rollouts):
per_success_rate = []
observation = env.reset()
obs = observation['observation']
g = observation['desired_goal']
for num in range(self.env_params['max_test_timesteps']):
with torch.no_grad():
act_obs, act_g = self._preproc_inputs(obs, g)
if num % self.c == 0:
hi_act_obs = np.concatenate((obs[:self.hi_dim], g))
hi_action = self.hi_agent.select_action(hi_act_obs, evaluate=True)
if self.old_sample:
new_hi_action = hi_action
else:
ag = self.representation(torch.Tensor(obs).to(self.device)).detach().cpu().numpy()[0]
new_hi_action = ag + hi_action
new_hi_action = np.clip(new_hi_action, -SUBGOAL_RANGE, SUBGOAL_RANGE)
hi_action_tensor = torch.tensor(new_hi_action, dtype=torch.float32).unsqueeze(0).to(self.device)
action = self.test_policy(act_obs[:, :self.low_dim], hi_action_tensor)
observation_new, rew, done, info = env.step(action)
if self.animate:
env.render()
obs = observation_new['observation']
g = observation_new['desired_goal']
if done:
per_success_rate.append(info['is_success'])
if bool(info['is_success']):
# print("t:", num)
discount_reward[roll] = 1 - 1. / self.env_params['max_test_timesteps'] * num
break
total_success_rate.append(per_success_rate)
total_success_rate = np.array(total_success_rate)
global_success_rate = np.mean(total_success_rate[:, -1])
global_reward = np.mean(discount_reward)
if self.args.eval:
print("hier success rate", global_success_rate, global_reward)
return global_success_rate, global_reward
def init_network(self):
self.low_actor_network = GaussianPolicy(self.env_params["low_dim"] + self.real_goal_dim, self.env_params['action'], self.args.hidden_size, self.low_act_space, None).to(self.device)
self.low_actor_target_network = GaussianPolicy(self.env_params["low_dim"] + self.real_goal_dim, self.env_params['action'], self.args.hidden_size, self.low_act_space, None).to(self.device)
self.low_critic_network = doubleWrapper(self.env_params, self.args).to(self.device)
self.low_critic_target_network = doubleWrapper(self.env_params, self.args).to(self.device)
self.start_epoch = 0
# create the optimizer
self.low_actor_optim = torch.optim.Adam(self.low_actor_network.parameters(), lr=self.args.lr_actor)
self.low_critic_optim = torch.optim.Adam(self.low_critic_network.parameters(), lr=self.args.lr_critic, weight_decay=1e-5)
def sync_target(self):
# load the weights into the target networks
self.low_actor_target_network.load_state_dict(self.low_actor_network.state_dict())
self.low_critic_target_network.load_state_dict(self.low_critic_network.state_dict())
def slow_update_phi(self, epoch):
sample_data, hi_action = self.slow_collect()
sample_data = torch.tensor(sample_data, dtype=torch.float32).to(self.device)
if not self.args.use_prediction:
obs, obs_next = self.representation(sample_data[0]), self.representation(sample_data[1])
min_dist = torch.clamp((obs - obs_next).pow(2).mean(dim=1), min=0.)
hi_obs, hi_obs_next = self.representation(sample_data[2]), self.representation(sample_data[3])
max_dist = torch.clamp(1 - (hi_obs - hi_obs_next).pow(2).mean(dim=1), min=0.)
representation_loss = (min_dist + max_dist).mean()
# add l2 regularization
representation_loss += self.feature_reg * (obs / self.abs_range).pow(2).mean()
else:
hi_action = torch.tensor(hi_action, dtype=torch.float32).to(self.device)
with torch.no_grad():
target_next_obs = self.representation.phi(sample_data[3])
obs, obs_next = self.representation.phi(sample_data[0]), self.representation.phi(sample_data[1])
min_dist = torch.clamp((obs - obs_next).pow(2).mean(dim=1), min=0.)
hi_obs, hi_obs_next = self.representation.phi(sample_data[2]), self.representation.phi(sample_data[3])
max_dist = torch.clamp(1 - (hi_obs - hi_obs_next).pow(2).mean(dim=1), min=0.)
representation_loss = (min_dist + max_dist).mean()
# prediction loss
if self.use_prob:
predict_distribution = self.representation(sample_data[2], hi_action)
prediction_loss = - predict_distribution.log_prob(target_next_obs).mean()
else:
predict_state = self.representation(sample_data[2], hi_action)
prediction_loss = (predict_state - target_next_obs).pow(2).mean()
representation_loss += self.prediction_coeff * prediction_loss
self.representation_optim.zero_grad()
representation_loss.backward()
self.representation_optim.step()
if self.args.save:
self.writer.add_scalar('Loss/phi_loss' + self.args.metric, representation_loss, epoch)
def slow_collect(self, batch_size=100):
if self.args.use_prediction:
transitions = self.low_buffer.sample(batch_size)
obs, obs_next = transitions['obs'], transitions['obs_next']
hi_obs, hi_action, _, hi_obs_next, _ = self.hi_buffer.sample(batch_size)
hi_obs, hi_obs_next = hi_obs[:, :self.env_params['obs']], hi_obs_next[:, :self.env_params['obs']]
train_data = np.array([obs, obs_next, hi_obs, hi_obs_next])
return train_data, hi_action
else:
# new negative samples
episode_num = self.low_buffer.current_size
obs_array = self.low_buffer.buffers['obs'][:episode_num]
episode_idxs = np.random.randint(0, episode_num, batch_size)
t_samples = np.random.randint(self.env_params['max_timesteps'] - self.k - self.delta_k, size=batch_size)
if self.delta_k > 0:
delta = np.random.randint(self.delta_k, size=batch_size)
else:
delta = 0
hi_obs = obs_array[episode_idxs, t_samples]
hi_obs_next = obs_array[episode_idxs, t_samples + self.k + delta]
obs = hi_obs
obs_next = obs_array[episode_idxs, t_samples + 1 + delta]
train_data = np.array([obs, obs_next, hi_obs, hi_obs_next])
return train_data, None
def visualize_representation(self, epoch):
transitions = self.low_buffer.sample(800)
obs = transitions['obs']
# with open('fig/final/' + "sampled_states.pkl", 'wb') as output:
# pickle.dump(obs, output)
index1 = np.where((obs[:, 0] < 4) & (obs[:, 1] < 4))
index2 = np.where((obs[:, 0] < 4) & (obs[:, 1] > 4))
index3 = np.where((obs[:, 0] > 4) & (obs[:, 1] < 4))
index4 = np.where((obs[:, 0] > 4) & (obs[:, 1] > 4))
index_lst = [index1, index2, index3, index4]
obs_tensor = torch.Tensor(obs).to(self.device)
features = self.representation(obs_tensor).detach().cpu().numpy()
plt.scatter(features[:, 0], features[:, 1], color='green')
plt.show()
# rep = []
# for index in index_lst:
# rep.append(features[index])
#
# self.plot_fig(rep, 'slow_feature', epoch)
#
#
# obs_list = []
# for index in index_lst:
# obs_list.append(obs[index])
# self.plot_fig(obs_list, 'obs', epoch)
'''
tsne_list = []
res_tsne = TSNE(n_components=2).fit_transform(obs)
for index in index_lst:
tsne_list.append(res_tsne[index])
self.plot_fig(tsne_list, 'tsne_feature', epoch)
'''
def plot_fig(self, rep, name, epoch):
fig = plt.figure()
axes = fig.add_subplot(111)
rep1, rep2, rep3, rep4 = rep
def scatter_rep(rep1, c, marker):
if rep1.shape[0] > 0:
l1 = axes.scatter(rep1[:, 0], rep1[:, 1], c=c, marker=marker)
else:
l1 = axes.scatter([], [], c=c, marker=marker)
return l1
l1 = scatter_rep(rep1, c='y', marker='s')
l2 = scatter_rep(rep2, c='r', marker='o')
l3 = scatter_rep(rep3, c='b', marker='1')
l4 = scatter_rep(rep4, c='g', marker='2')
plt.xlabel('x')
plt.ylabel('y')
axes.legend((l1, l2, l3, l4), ('space1', 'space2', 'space3', 'space4'))
plt.savefig('fig/final/' + name + str(epoch) + '.png')
plt.close()
def vis_hier_policy(self, epoch=0, load_obs=None, color_map='RdYlBu'):
obs_vec = []
hi_action_vec = []
env = self.test_env
observation = env.reset()
obs = observation['observation']
obs_vec.append(obs)
g = observation['desired_goal']
if load_obs is None:
for num in range(self.env_params['max_test_timesteps']):
with torch.no_grad():
act_obs, act_g = self._preproc_inputs(obs, g)
if num % self.c == 0:
hi_act_obs = np.concatenate((obs[:self.hi_dim], g))
hi_action = self.hi_agent.select_action(hi_act_obs, evaluate=True)
hi_action_tensor = torch.tensor(hi_action, dtype=torch.float32).unsqueeze(0).to(self.device)
ag = self.representation(torch.Tensor(obs).to(self.device)).detach().cpu().numpy()[0]
distance = np.linalg.norm(hi_action - ag)
print("distance", distance)
hi_action_vec.append(hi_action)
action = self.test_policy(act_obs[:, :self.low_dim], hi_action_tensor)
observation_new, rew, done, info = env.step(action)
if self.animate:
env.render()
obs = observation_new['observation']
obs_vec.append(obs)
if done:
if info['is_success']:
print("success !!!")
break
else:
obs_vec = load_obs[0]
plt.figure(figsize=(12, 6))
obs_vec = np.array(obs_vec)
with open('fig/final/' + "img_push_hard.pkl", 'wb') as output:
pickle.dump(obs_vec, output)
self.plot_rollout(obs_vec, "XY_{}".format(epoch * self.env_params['max_timesteps']), 121, goal=g)
if not self.learn_goal_space:
features = obs_vec[:, :2]
feature_goal = g[:2]
else:
obs_tensor = torch.Tensor(obs_vec[:, :self.hi_dim]).to(self.device)
features = self.representation(obs_tensor).detach().cpu().numpy()
# rest = (self.env_params['obs'] - self.env_params['goal']) * [0.]
# g = np.concatenate((g, np.array(rest)))
# g = torch.tensor(g, dtype=torch.float32).unsqueeze(0).to(self.device)
# feature_goal = self.representation(g).detach().cpu().numpy()[0]
feature_goal = None
hi_action_vec = np.array(hi_action_vec)
self.plot_rollout(features, "Feature_{}".format(epoch * self.env_params['max_timesteps']), 122, feature_goal, color_map="Blues",
hi_action_vec = hi_action_vec)
if load_obs is not None and len(load_obs) > 1:
obs_vec = load_obs[1]
obs_tensor = torch.Tensor(obs_vec[:, :self.hi_dim]).to(self.device)
features = self.representation(obs_tensor).detach().cpu().numpy()
self.plot_rollout(features, "Feature_{}".format(epoch * self.env_params['max_timesteps']), 122, feature_goal,
color_map="Wistia")
file_name = 'fig/rebuttal/rollout' + str(epoch) + '.png'
plt.savefig(file_name, bbox_inches='tight', transparent=True)
# plt.show()
plt.close()
def plot_rollout(self, obs_vec, name, num, goal=None, hi_action_vec=None, no_axis=True, color_map='RdYlBu'):
plt.subplot(num)
cm = plt.cm.get_cmap(color_map)
num = np.arange(obs_vec.shape[0])
plt.scatter(obs_vec[:, 0], obs_vec[:, 1], c=num, cmap=cm)
if goal is not None:
plt.scatter([goal[0]], [goal[1]], marker='*',
color='green', s=200, label='goal')
if hi_action_vec is not None:
plt.scatter(hi_action_vec[:, 0], hi_action_vec[:, 1], c="k")
plt.title(name, fontsize=24)
if no_axis:
plt.axis('off')
if not no_axis:
plt.scatter([obs_vec[0, 0]], [obs_vec[0, 1]], marker='+',
color='green', s=200, label='start')
plt.scatter([obs_vec[-1, 0]], [obs_vec[-1, 1]], marker='+',
color='red', s=200, label='end')
plt.legend(loc=2, bbox_to_anchor=(1.05, 1.0), fontsize=14, borderaxespad=0.)
# plt.show()
algos/sac/model.py
......@@ -200,6 +200,23 @@ class GaussianPolicy(nn.Module):
mean = torch.tanh(mean) * self.action_scale + self.action_bias
return action, log_prob, mean
def correct(self, state_candidate, action):
candidate_num = state_candidate.shape[1]
mean, log_std = self.forward(state_candidate)
std = log_std.exp()
normal = Normal(mean, std)
x_t = torch.arctanh((action - self.action_bias) / self.action_scale)
x_t = x_t.unsqueeze(1).expand(-1, candidate_num, -1, -1)
# print("x_t", x_t.shape)
log_prob = normal.log_prob(x_t)
# print("log_prob:", log_prob.shape)
log_prob = log_prob.sum(-1).sum(-1)
# print("log_prob:", log_prob.shape)
correct_index = log_prob.argmax(1, keepdim=True)
# print("correct_index:", correct_index.shape)
return correct_index
def to(self, device):
self.action_scale = self.action_scale.to(device)
self.action_bias = self.action_bias.to(device)
......
algos/sac/replay_memory.py
......@@ -2,21 +2,30 @@ import random
import numpy as np
class ReplayMemory:
def __init__(self, capacity):
def __init__(self, capacity, use_goal_correct=False):
self.capacity = capacity
self.buffer = []
self.position = 0
self.use_goal_correct = use_goal_correct
def push(self, state, action, reward, next_state, done, epoch):
def push(self, state, action, reward, next_state, done, epoch, state_c_step=None, low_action=None):
if len(self.buffer) < self.capacity:
self.buffer.append(None)
if not self.use_goal_correct:
self.buffer[self.position] = (state, action, reward, next_state, done, epoch+1)
else:
assert not low_action == None
self.buffer[self.position] = (state, action, reward, next_state, done, epoch+1, state_c_step, low_action)
self.position = (self.position + 1) % self.capacity
def sample(self, batch_size):
batch = random.sample(self.buffer, batch_size)
if not self.use_goal_correct:
state, action, reward, next_state, done, _ = map(np.stack, zip(*batch))
return state, action, reward, next_state, done
else:
state, action, reward, next_state, done, _ , state_c_step, low_action= map(np.stack, zip(*batch))
return state, action, reward, next_state, done, state_c_step, low_action
def __len__(self):
return len(self.buffer)
......@@ -36,8 +45,12 @@ class ReplayMemory:
p_trajectory = p_trajectory.astype(np.float64)
idxs = np.random.choice(len(self.buffer), size=batch_size, replace=False, p=p_trajectory)
batch = [self.buffer[i] for i in idxs]
if not self.use_goal_correct:
state, action, reward, next_state, done, _ = map(np.stack, zip(*batch))
return state, action, reward, next_state, done
else:
state, action, reward, next_state, done, _ , state_c_step, low_action= map(np.stack, zip(*batch))
return state, action, reward, next_state, done, state_c_step, low_action
def random_sample(self, batch_size):
idxs = np.random.randint(0, len(self.buffer), batch_size)
......
algos/sac/sac.py
......@@ -4,10 +4,10 @@ import torch.nn.functional as F
from torch.optim import Adam
from algos.sac.utils import soft_update, hard_update
from algos.sac.model import GaussianPolicy, QNetwork, DeterministicPolicy, QNetwork_phi
import numpy as np
class SAC(object):
def __init__(self, num_inputs, action_space, args, pri_replay, goal_dim, gradient_flow_value, abs_range, tanh_output):
def __init__(self, num_inputs, action_space, args, pri_replay, goal_dim, gradient_flow_value, abs_range, tanh_output, use_goal_correct=False):
self.gamma = args.gamma
self.tau = args.tau
......@@ -20,6 +20,7 @@ class SAC(object):
self.device = args.device
self.gradient_flow_value = gradient_flow_value
self.use_goal_correct = use_goal_correct
if not gradient_flow_value:
self.critic = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
......@@ -64,18 +65,41 @@ class SAC(object):
_, _, action = self.policy.sample(state)
return action.detach().cpu().numpy()[0]
def update_parameters(self, memory, batch_size, env_params, hi_sparse, feature_data):
def select_num_action(self, state, num):
action_candidate = np.array([])
batch = state.shape[0]
for i in range(num):
action, _, _ = self.policy.sample(state)
action_candidate = np.append(action_candidate, action.detach().cpu().numpy())
return action_candidate.reshape(batch, num, -1)
def update_parameters(self, memory, batch_size, env_params, hi_sparse, feature_data, low_policy=None, representation=None):
# Sample a batch from memory
if self.pri_replay:
if not self.use_goal_correct:
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.pri_sample(batch_size=batch_size)
else:
state_batch, action_batch, reward_batch, next_state_batch, mask_batch, low_state_c_step_batch, low_action_batch = memory.pri_sample(batch_size=batch_size)
else:
if not self.use_goal_correct:
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(batch_size=batch_size)
else:
state_batch, action_batch, reward_batch, next_state_batch, mask_batch, low_state_c_step_batch, low_action_batch = memory.sample(batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
low_state_c_step_batch = torch.FloatTensor(low_state_c_step_batch).to(self.device)
low_action_batch = torch.FloatTensor(low_action_batch).to(self.device)
# print("state_batch shape:", state_batch.shape)
# print("action_batch shape:", action_batch.shape)
# print("reward_batch:", reward_batch.shape)
# print("mask_batch:", mask_batch.shape)
# print("low_state_c_step_batch shape:", low_state_c_step_batch.shape)
# print("low_action_batch shape:", low_action_batch.shape)
with torch.no_grad():
next_state_action, next_state_log_pi, _ = self.policy.sample(next_state_batch)
......@@ -86,7 +110,32 @@ class SAC(object):
if hi_sparse:
# clip target value
next_q_value = torch.clamp(next_q_value, -env_params['max_timesteps'], 0.)
qf1, qf2 = self.critic(state_batch, action_batch) # Two Q-functions to mitigate positive bias in the policy improvement step
hi_action_candidate_num = 10
c_step = low_state_c_step_batch.shape[1]
real_goal_dim = env_params["real_goal_dim"]
if self.use_goal_correct:
with torch.no_grad():
action_batch_candidate = torch.FloatTensor(self.select_num_action(state_batch, hi_action_candidate_num-2)).to(self.device)
# print("action_batch_candidate:", action_batch_candidate.shape)
mean, _ = self.policy(state_batch)
# print("mean:", mean.shape)
action_batch_candidate = torch.cat([action_batch_candidate, action_batch.unsqueeze(1), mean.unsqueeze(1)], dim=1)
# print("action_batch_candidate:", action_batch_candidate.shape)
ag = representation(state_batch[:, :env_params["obs"]]).unsqueeze(1)
# print("ag shape:", ag.shape)
goal_batch_candidate = action_batch_candidate + ag
low_state_batch_candidate = torch.cat([low_state_c_step_batch.unsqueeze(1).expand(-1, hi_action_candidate_num, -1, -1), goal_batch_candidate.unsqueeze(2).expand(-1, -1, c_step, -1)], dim=-1)
# print("low_state_batch_candidate:", low_state_batch_candidate.shape)
goal_correct_index = low_policy.correct(low_state_batch_candidate, low_action_batch)
goal_correct_index = goal_correct_index.expand(-1, hi_action_candidate_num * real_goal_dim).reshape(-1, hi_action_candidate_num, real_goal_dim)
action_batch_correct = torch.gather(action_batch_candidate, 1, goal_correct_index)[:,0,:]
# print("action_batch_candidate:", action_batch_candidate)
# print("action_batch_correct:", action_batch_correct)
# print("action_batch:", action_batch)
qf1, qf2 = self.critic(state_batch, action_batch_correct) # Two Q-functions to mitigate positive bias in the policy improvement step
# print("qf1", qf1.shape)
# print("next_q", next_q_value.shape)
qf1_loss = F.mse_loss(qf1, next_q_value) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
......
arguments/arguments_hier_sac.py
......@@ -18,7 +18,7 @@ def get_args_ant():
parser.add_argument('--seed', type=int, default=125, help='random seed')
parser.add_argument('--replay-strategy', type=str, default='none', help='the HER strategy')
parser.add_argument('--save-dir', type=str, default='saved_models/', help='the path to save the models')
parser.add_argument('--save-dir', type=str, default='/lustre/S/gaoyunkai/RL/LESSON/saved_models/', help='the path to save the models')
parser.add_argument('--noise-eps', type=float, default=0.2, help='noise factor for Gaussian')
parser.add_argument('--random-eps', type=float, default=0.2, help="prob for acting randomly")
......
goal_env/mujoco/__init__.py
......@@ -10,6 +10,8 @@ elif sys.argv[0].split('/')[-1] == "train_hier_sac.py":
from train_hier_sac import args
elif sys.argv[0].split('/')[-1] == "train_hier_double_sac.py":
from train_hier_double_sac import args
elif sys.argv[0].split('/')[-1] == "train_hier_double_sac_goal_correct.py":
from train_hier_double_sac import args
elif sys.argv[0].split('/')[-1] == "train_hier_ppo.py":
from train_hier_ppo import args
elif sys.argv[0].split('/')[-1] == "train_covering.py":
......
train_hier_double_sac_goal_correct.py 0 → 100644
import numpy as np
import gym
from arguments.arguments_hier_sac import get_args_ant, get_args_chain
from algos.hier_double_sac_goal_correct import hier_sac_agent
from goal_env.mujoco import *
import random
import torch
def get_env_params(env):
obs = env.reset()
# close the environment
params = {'obs': obs['observation'].shape[0], 'goal': obs['desired_goal'].shape[0],
'action': env.action_space.shape[0], 'action_max': env.action_space.high[0],
'max_timesteps': env._max_episode_steps}
return params
def launch(args):
# create the ddpg_agent
env = gym.make(args.env_name)
test_env = gym.make(args.test)
# if args.env_name == "AntPush-v1":
# test_env1 = gym.make("AntPushTest1-v1")
# test_env2 = gym.make("AntPushTest2-v1")
# elif args.env_name == "AntMaze1-v1":
# test_env1 = gym.make("AntMaze1Test1-v1")
# test_env2 = gym.make("AntMaze1Test2-v1")
# else:
test_env1 = test_env2 = None
print("test_env", test_env1, test_env2)
# set random seeds for reproduce
env.seed(args.seed)
if args.env_name != "NChain-v1":
env.env.env.wrapped_env.seed(args.seed)
test_env.env.env.wrapped_env.seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.device is not 'cpu':
torch.cuda.manual_seed(args.seed)
gym.spaces.prng.seed(args.seed)
# get the environment parameters
if args.env_name[:3] in ["Ant", "Poi", "Swi"]:
env.env.env.visualize_goal = args.animate
test_env.env.env.visualize_goal = args.animate
env_params = get_env_params(env)
env_params['max_test_timesteps'] = test_env._max_episode_steps
# create the ddpg agent to interact with the environment
sac_trainer = hier_sac_agent(args, env, env_params, test_env, test_env1, test_env2)
if args.eval:
if not args.resume:
print("random policy !!!")
# sac_trainer._eval_hier_agent(test_env)
# sac_trainer.vis_hier_policy()
# sac_trainer.cal_slow()
# sac_trainer.visualize_representation(100)
# sac_trainer.vis_learning_process()
# sac_trainer.picvideo('fig/final/', (1920, 1080))
else:
sac_trainer.learn()
# get the params
args = get_args_ant()
# args = get_args_chain()
# args = get_args_fetch()
# args = get_args_point()
if __name__ == '__main__':
launch(args)
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