def main(scene_idx=0, point_a=0):
#scene_idx = 1
## necessary constants
mapper_scene2points = get_mapper_scene2points()
num_episodes = 200000
batch_size = 64
lambda_action = 0.25
action_table = buildActionMapper(flag_fewer_actions=True)
seq_len = 50
Train_Scenes, Test_Scenes = get_train_test_scenes()
if mode == 'Test':
scene_name = Test_Scenes[scene_idx]
elif mode == 'Train':
scene_name = Train_Scenes[scene_idx]
num_startPoints = len(mapper_scene2points[scene_name])
model_weights_save_path = '{}/{}'.format('/home/reza/Datasets/GibsonEnv/my_code/vs_controller/trained_dqn', approach)
action_space = action_table.shape[0]
##=============================================================================================================
## rrt functions
## first figure out how to sample points from rrt graph
rrt_directory = '/home/reza/Datasets/GibsonEnv/gibson/assets/dataset/{}_for_rrt'.format(scene_name)
path_finder = rrt.PathFinder(rrt_directory)
path_finder.load()
num_nodes = len(path_finder.nodes_x)
free = cv2.imread('/home/reza/Datasets/GibsonEnv/gibson/assets/dataset/{}_for_rrt/free.png'.format(scene_name), 0)
##------------------------------------------------------------------------------------------------------------
## setup environment
import gym, logging
from mpi4py import MPI
from gibson.envs.husky_env import HuskyNavigateEnv
from baselines import logger
import skimage.io
from transforms3d.euler import euler2quat
config_file = os.path.join('/home/reza/Datasets/GibsonEnv/my_code/CVPR_workshop', 'env_yamls', '{}_navigate.yaml'.format(scene_name))
env = HuskyNavigateEnv(config=config_file, gpu_count = 1)
obs = env.reset() ## this line is important otherwise there will be an error like 'AttributeError: 'HuskyNavigateEnv' object has no attribute 'potential''
mapper_scene2z = get_mapper()
def get_obs(current_pose):
pos, orn = func_pose2posAndorn(current_pose, mapper_scene2z[scene_name])
env.robot.reset_new_pose(pos, orn)
obs, _, _, _ = env.step(4)
obs_rgb = obs['rgb_filled']
obs_depth = obs['depth']
#obs_normal = obs['normal']
return obs_rgb, obs_depth#, obs_normal
def close_to_goal(pose1, pose2, thresh=0.20):
L2_dist = math.sqrt((pose1[0] - pose2[0])**2 + (pose1[1] - pose2[1])**2)
thresh_L2_dist = thresh
theta_change = abs(pose1[2] - pose2[2])/math.pi * 180
return (L2_dist <= thresh_L2_dist) #and (theta_change <= 30)
##============================================================================================================
if mode == 'Test':
base_folder = '/home/reza/Datasets/GibsonEnv/my_code/visual_servoing/sample_image_pairs_{}'.format('test')
elif mode == 'Train':
base_folder = '/home/reza/Datasets/GibsonEnv/my_code/visual_servoing/sample_image_pairs_{}'.format('train')
import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device('cuda:0') ## Default CUDA device
num_epochs = 200000 ## same as # of trajs sampled
num_actions = action_table.shape[0]
if input_type == 'both':
perception = Perception_overlap(4).to(device)
elif input_type == 'siamese':
perception = Perception_siamese(4).to(device)
elif input_type == 'optical_flow':
perception = Perception_overlap(2).to(device)
elif input_type == 'optical_flow_depth':
perception = Perception_overlap(3).to(device)
elif input_type == 'optical_flow_depth_normalized':
perception = Perception_overlap(3).to(device)
elif input_type == 'optical_flow_depth_unnormalized_mask':
perception = Perception_overlap(3).to(device)
elif input_type == 'optical_flow_depth_siamese':
perception = Perception_siamese_fusion_new(3).to(device)
elif input_type == 'optical_flow_memory':
perception = Preception_overlap_resnet(4).to(device)
else:
perception = Perception_overlap(2).to(device)
if input_type == 'siamese':
model = DQN_OVERLAP_Controller(perception, num_actions, input_size=512).to(device)
elif input_type == 'optical_flow_memory':
model = DQN_OVERLAP_RESNET_Controller(perception, num_actions, input_size=512).to(device)
else:
model = DQN_OVERLAP_Controller(perception, num_actions, input_size=256).to(device)
model.load_state_dict(torch.load('{}/dqn_epoch_{}_Uvalda.pt'.format(model_weights_save_path, num_epochs)))
#model.eval()
list_succ = []
list_collision = []
## go through each point folder
if mode == 'Test':
a, b = 0, 1
elif mode == 'Train':
a, b = 7, 8
#a, b = 0, 1
#for point_idx in range(0, num_startPoints):
#for point_idx in range(a, b):
for point_idx in range(point_a, point_a+1):
print('point_idx = {}'.format(point_idx))
task_folder = '{}/{}/point_{}'.format('/home/reza/Datasets/GibsonEnv/my_code/vs_controller/for_video', scene_name, point_idx)
create_folder(task_folder)
## read in start img and start pose
point_image_folder = '{}/{}/point_{}'.format(base_folder, scene_name, point_idx)
point_pose_npy_file = np.load('{}/{}/point_{}_poses.npy'.format(base_folder, scene_name, point_idx))
#start_img = cv2.imread('{}/{}.png'.format(point_image_folder, point_pose_npy_file[0]['img_name']))[:, :, ::-1]
start_pose = point_pose_npy_file[0]['pose']
start_img, start_depth = get_obs(start_pose)
start_depth = start_depth.copy()
count_succ = 0
count_collision = 0
count_short_runs = 0
count_short_runs_collision = 0
count_short_runs_succ = 0
## index 0 is the left image, so right_img_idx starts from index 1
#for right_img_idx in range(1, len(point_pose_npy_file)):
for right_img_idx in range(1, 101):
print('right_img_idx = {}'.format(right_img_idx))
run_folder = '{}/run_{}'.format(task_folder, right_img_idx)
create_folder(run_folder)
current_pose = start_pose
right_img_name = point_pose_npy_file[right_img_idx]['img_name']
goal_pose = point_pose_npy_file[right_img_idx]['pose']
#goal_img = cv2.imread('{}/{}.png'.format(point_image_folder, right_img_name), 1)[:,:,::-1]
goal_img, goal_depth = get_obs(goal_pose)
goal_depth = goal_depth.copy()
current_depth = start_depth.copy()
episode_reward = 0
flag_succ = False
poses_list = []
poses_list.append(start_pose)
poses_list.append(goal_pose)
poses_list.append([current_pose])
for i_step in range(seq_len):
if input_type == 'both' or input_type == 'siamese':
overlapArea_currentView = genOverlapAreaOnCurrentView(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2]
overlapArea_goalView = genOverlapAreaOnGoalView(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2]
overlapArea = np.concatenate((overlapArea_currentView, overlapArea_goalView), axis=2)
elif input_type == 'optical_flow':
overlapArea = genGtDenseCorrespondenseFlowMap(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2]
overlapArea = removeCorrespondenceRandomly(overlapArea, keep_prob=1.0)
elif input_type == 'optical_flow_depth':
opticalFlow = genGtDenseCorrespondenseFlowMap(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2]
overlapArea = np.concatenate((opticalFlow, current_depth), axis=2)
elif input_type == 'optical_flow_depth_normalized':
opticalFlow = genGtDenseCorrespondenseFlowMap(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2]
normalized_opticalFlow = normalize_opticalFlow(opticalFlow)
normalized_depth = normalize_depth(current_depth)
#normalized_depth = np.ones((256, 256, 1), np.float32)
overlapArea = np.concatenate((normalized_opticalFlow, normalized_depth), axis=2)
elif input_type == 'optical_flow_depth_unnormalized_mask':
opticalFlow, mask_flow = genGtDenseCorrespondenseFlowMapAndMask(current_depth, goal_depth, current_pose, goal_pose)
opticalFlow = opticalFlow[:, :, :2]
normalized_depth = current_depth * mask_flow
#normalized_opticalFlow = normalize_opticalFlow(opticalFlow)
normalized_depth = normalize_depth(normalized_depth)
overlapArea = np.concatenate((opticalFlow, normalized_depth), axis=2)
elif input_type == 'optical_flow_depth_siamese':
opticalFlow = genGtDenseCorrespondenseFlowMap(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2]
normalized_depth = normalize_depth(current_depth)
#normalized_depth = np.ones((256, 256, 1), np.float32)
overlapArea = np.concatenate((opticalFlow, normalized_depth), axis=2)
#print('overlapArea.shape = {}'.format(overlapArea.shape))
elif input_type == 'optical_flow_memory':
opticalFlow = genGtDenseCorrespondenseFlowMap(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2]
if i_step == 0:
overlapArea = np.concatenate((opticalFlow, opticalFlow), axis=2)
else:
overlapArea = np.concatenate((old_opticalFlow, opticalFlow), axis=2)
else:
overlapArea = genOverlapAreaOnCurrentView(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2]
tensor_left = torch.tensor(overlapArea, dtype=torch.float32).to(device).unsqueeze(0).permute(0, 3, 1, 2)
Qvalue_table = model(tensor_left)
pred = Qvalue_table.max(1)[1].view(1, 1).detach().cpu().numpy().item() ## batch_size x 3
#print('Qvalue_table: {}'.format(Qvalue_table))
#print('pred = {}'.format(pred))
## update current_pose
vz, omegay = action_table[pred]
#print('vz = {:.2f}, omegay = {:.2f}'.format(vz, omegay))
vx = 0.0
vx = vx * lambda_action
vz = vz * lambda_action
omegay = omegay * pi * lambda_action
#print('actual velocity = {:.2f}, {:.2f}, {:.2f}'.format(vx, vz, omegay))
previous_pose = current_pose
current_pose = update_current_pose(current_pose, vx, vz, omegay)
poses_list[2].append(current_pose)
flag_broken = False
left_pixel = path_finder.point_to_pixel((previous_pose[0], previous_pose[1]))
right_pixel = path_finder.point_to_pixel((current_pose[0], current_pose[1]))
## rrt.line_check returns True when there is no obstacle
if not rrt.line_check(left_pixel, right_pixel, free):
print('run into something')
flag_broken = True
break
if close_to_goal(current_pose, goal_pose):
print('success run')
flag_succ = True
break
## compute new_state
current_img, current_depth = get_obs(current_pose)
current_depth = current_depth.copy()
#old_opticalFlow = opticalFlow.copy()
np.save('{}/{}_waypoint_pose_list.npy'.format(run_folder, right_img_name[10:]), poses_list)
#assert 1==2
if flag_succ:
count_succ += 1
list_succ.append(point_pose_npy_file[right_img_idx]['img_name'])
if findShortRangeImageName(right_img_name):
count_short_runs_succ += 1
if flag_broken:
count_collision += 1
list_collision.append(point_pose_npy_file[right_img_idx]['img_name'])
if findShortRangeImageName(right_img_name):
count_short_runs_collision += 1
if findShortRangeImageName(right_img_name):
count_short_runs += 1
print('count_succ = {}'.format(count_succ))
print('count_collision = {}'.format(count_collision))
print('count_short_runs_succ = {}'.format(count_short_runs_succ))
print('count_short_runs_collision = {}'.format(count_short_runs_collision))
print('num_succ = {}, num_run = {}, count_short_runs_succ = {}, count_short_runs = {}'.format(count_succ, len(point_pose_npy_file), count_short_runs_succ, count_short_runs))
elif input_type == 'optical_flow':
overlapArea = genGtDenseCorrespondenseFlowMap(
current_depth, goal_depth, current_pose,
goal_pose)[:, :, :2]
elif input_type == 'optical_flow_depth':
opticalFlow = genGtDenseCorrespondenseFlowMap(
current_depth, goal_depth, current_pose,
goal_pose)[:, :, :2]
overlapArea = np.concatenate((opticalFlow, current_depth),
axis=2)
elif input_type == 'optical_flow_depth_normalized':
opticalFlow = genGtDenseCorrespondenseFlowMap(
current_depth, goal_depth, current_pose,
goal_pose)[:, :, :2]
normalized_opticalFlow = normalize_opticalFlow(opticalFlow)
normalized_depth = normalize_depth(current_depth)
#normalized_depth = np.ones((256, 256, 1), np.float32)
overlapArea = np.concatenate(
(normalized_opticalFlow, normalized_depth), axis=2)
elif input_type == 'optical_flow_depth_unnormalized_mask':
opticalFlow, mask_flow = genGtDenseCorrespondenseFlowMapAndMask(
current_depth, goal_depth, current_pose, goal_pose)
opticalFlow = opticalFlow[:, :, :2]
normalized_depth = current_depth * mask_flow
#normalized_opticalFlow = normalize_opticalFlow(opticalFlow)
normalized_depth = normalize_depth(normalized_depth)
overlapArea = np.concatenate((opticalFlow, normalized_depth),
axis=2)
elif input_type == 'optical_flow_depth_siamese':
opticalFlow = genGtDenseCorrespondenseFlowMap(
current_depth, goal_depth, current_pose,
def main(scene_idx=0, actual_episodes=1):
#scene_idx = 0
#actual_episodes=1
Train_Scenes, Test_Scenes = get_train_test_scenes()
scene_name = Train_Scenes[scene_idx]
num_startPoints = len(mapper_scene2points[scene_name])
model_weights_save_path = '{}'.format(
'/home/reza/Datasets/GibsonEnv/my_code/vs_controller/trained_dqn')
action_space = action_table.shape[0]
##=============================================================================================================
## rrt functions
## first figure out how to sample points from rrt graph
rrt_directory = '/home/reza/Datasets/GibsonEnv/gibson/assets/dataset/{}_for_rrt'.format(
scene_name)
path_finder = rrt.PathFinder(rrt_directory)
path_finder.load()
num_nodes = len(path_finder.nodes_x)
free = cv2.imread(
'/home/reza/Datasets/GibsonEnv/gibson/assets/dataset/{}_for_rrt/free.png'
.format(scene_name), 0)
##------------------------------------------------------------------------------------------------------------
## setup environment
import gym, logging
from mpi4py import MPI
from gibson.envs.husky_env import HuskyNavigateEnv
from baselines import logger
import skimage.io
from transforms3d.euler import euler2quat
config_file = os.path.join(
'/home/reza/Datasets/GibsonEnv/my_code/CVPR_workshop', 'env_yamls',
'{}_navigate.yaml'.format(scene_name))
env = HuskyNavigateEnv(config=config_file, gpu_count=1)
obs = env.reset(
) ## this line is important otherwise there will be an error like 'AttributeError: 'HuskyNavigateEnv' object has no attribute 'potential''
mapper_scene2z = get_mapper()
def get_obs(current_pose):
pos, orn = func_pose2posAndorn(current_pose,
mapper_scene2z[scene_name])
env.robot.reset_new_pose(pos, orn)
obs, _, _, _ = env.step(4)
obs_rgb = obs['rgb_filled']
obs_depth = obs['depth']
#obs_normal = obs['normal']
return obs_rgb, obs_depth #, obs_normal
def close_to_goal(pose1, pose2, thresh=0.15):
L2_dist = math.sqrt((pose1[0] - pose2[0])**2 +
(pose1[1] - pose2[1])**2)
thresh_L2_dist = thresh
theta_change = abs(pose1[2] - pose2[2]) / math.pi * 180
return (L2_dist < thresh_L2_dist) and (theta_change <= 30)
def compute_distance_old(left_pose, right_pose, lamb=0.5):
x1, y1 = left_pose[0], left_pose[1]
a1, b1 = cos(left_pose[2]), sin(left_pose[2])
x2, y2 = right_pose[0], right_pose[1]
a2, b2 = cos(right_pose[2]), sin(right_pose[2])
x_y_dist = math.sqrt((x1 - x2)**2 + (y1 - y2)**2)
theta_dist = math.sqrt((a1 - a2)**2 + (b1 - b2)**2)
return x_y_dist + lamb * theta_dist
def compute_distance(left_pose, right_pose, lamb_alpha=0.5, lamb_beta=0.2):
x1, y1 = left_pose[0], left_pose[1]
x2, y2 = right_pose[0], right_pose[1]
pho_dist = math.sqrt((x1 - x2)**2 + (y1 - y2)**2)
left_pose_heading = left_pose[2]
right_pose_heading = right_pose[2]
location_angle = atan2(y2 - y1, x2 - x1)
print(
'left_pose_heading = {}, right_pose_heading = {}, location_angle = {}'
.format(left_pose_heading, right_pose_heading, location_angle))
if pho_dist >= 0.05:
## alpha angle in goToPose is the difference between location angle and left_pose_heading
a1, b1 = cos(location_angle), sin(location_angle)
a2, b2 = cos(left_pose_heading), sin(left_pose_heading)
alpha_dist = math.sqrt((a1 - a2)**2 + (b1 - b2)**2)
## beta angle in goToPose is the difference between right_pose_heading and location angle
a1, b1 = cos(right_pose_heading), sin(right_pose_heading)
a2, b2 = cos(location_angle), sin(location_angle)
beta_dist = math.sqrt((a1 - a2)**2 + (b1 - b2)**2)
else:
## when pho_dist is close to zero, alpha_dist is not important
alpha_dist = 0.0
## beta angle becomes the anlge between left and right poses
a1, b1 = cos(right_pose_heading), sin(right_pose_heading)
a2, b2 = cos(left_pose_heading), sin(left_pose_heading)
beta_dist = math.sqrt((a1 - a2)**2 + (b1 - b2)**2)
print('pho_dist = {:.2f}, alpha_dist = {:.2f}, beta_dist = {:.2f}'.
format(pho_dist, alpha_dist, beta_dist))
return pho_dist + lamb_alpha * alpha_dist + lamb_beta * beta_dist
def decide_reward_and_done(previous_pose, current_pose, goal_pose,
start_pose):
## check if the new step is on free space or not
reward = 0.0
done = 0
## check if current_pose is closer to goal_pose than previous_pose
'''
L2_dist_current = math.sqrt((current_pose[0] - goal_pose[0])**2 + (current_pose[1] - goal_pose[1])**2)
L2_dist_previous = math.sqrt((previous_pose[0] - goal_pose[0])**2 + (previous_pose[1] - goal_pose[1])**2)
if L2_dist_current < L2_dist_previous:
reward += 0.25
print('L2_dist_current = {:.2f}, L2_dist_previous = {:.2f}, reward = {}'.format(L2_dist_current, L2_dist_previous, reward))
'''
## following Fereshteh's DiVIs paper
dist_init = compute_distance(start_pose, goal_pose, lamb_alpha=0.2)
dist_current = compute_distance(current_pose,
goal_pose,
lamb_alpha=0.2)
reward = max(0,
1 - min(dist_init, dist_current) / (dist_init + 0.0001))
print('dist_init = {:.2f}, dist_current = {:.2f}, reward = {:.2f}'.
format(dist_init, dist_current, reward))
## check if current_pose is close to goal
## goal reward should be larger than all the previously accumulated reward
flag_close_to_goal = close_to_goal(current_pose, goal_pose)
if flag_close_to_goal:
reward = 50.0
done = 1
print(
'current_pose = {}, goal_pose = {}, flag_close_to_goal = {}, reward = {}'
.format(current_pose, goal_pose, flag_close_to_goal, reward))
#collision_done = 0
## if there is a collision, reward is -1 and the episode is done
left_pixel = path_finder.point_to_pixel(
(previous_pose[0], previous_pose[1]))
right_pixel = path_finder.point_to_pixel(
(current_pose[0], current_pose[1]))
## rrt.line_check returns True when there is no obstacle
if not rrt.line_check(left_pixel, right_pixel, free):
print('bumped into obstacle ....')
#reward = 0.0
#collision_done = 1
done = 1
print('final reward = {}'.format(reward))
return reward, done, 0 #, collision_done
##============================================================================================================
base_folder = '/home/reza/Datasets/GibsonEnv/my_code/visual_servoing/sample_image_pairs_{}'.format(
'train')
#agent = DQN_vs_overlap(trained_model_path=None, num_actions=action_space, input_channels=2)
agent = DQN_vs_overlap(trained_model_path=model_weights_save_path,
num_actions=action_space,
input_channels=2)
rewards = []
avg_rewards = []
for i_epoch in range(actual_episodes):
## go through each point folder
for point_idx in range(0, num_startPoints):
#for point_idx in range(0, 1):
print('point_idx = {}'.format(point_idx))
## read in start img and start pose
point_image_folder = '{}/{}/point_{}'.format(
base_folder, scene_name, point_idx)
point_pose_npy_file = np.load('{}/{}/point_{}_poses.npy'.format(
base_folder, scene_name, point_idx))
#start_img = cv2.imread('{}/{}.png'.format(point_image_folder, point_pose_npy_file[0]['img_name']))[:, :, ::-1]
start_pose = point_pose_npy_file[0]['pose']
start_img, start_depth = get_obs(start_pose)
start_depth = start_depth.copy()
## index 0 is the left image, so right_img_idx starts from index 1
for right_img_idx in range(1, len(point_pose_npy_file)):
#for right_img_idx in range(3, 4):
#print('right_img_idx = {}'.format(right_img_idx))
current_pose = start_pose
right_img_name = point_pose_npy_file[right_img_idx]['img_name']
goal_pose = point_pose_npy_file[right_img_idx]['pose']
#goal_img = cv2.imread('{}/{}.png'.format(point_image_folder, right_img_name), 1)[:,:,::-1]
goal_img, goal_depth = get_obs(goal_pose)
goal_img, goal_depth = goal_img.copy(), goal_depth.copy()
opticalFlow = genGtDenseCorrespondenseFlowMap(
start_depth, goal_depth, start_pose, goal_pose)[:, :, :2]
normalized_depth = normalize_depth(start_depth)
overlapArea = opticalFlow * normalized_depth
state = [overlapArea]
#assert 1==2
episode_reward = 0
for i_step in range(seq_len):
action = agent.select_action(state)
print('action = {}'.format(action))
## update current_pose
vz, omegay = action_table[action]
print('vz = {:.2f}, omegay = {:.2f}'.format(vz, omegay))
vx = 0.0
vx = vx * lambda_action
vz = vz * lambda_action
omegay = omegay * pi * lambda_action
print('actual velocity = {:.2f}, {:.2f}, {:.2f}'.format(
vx, vz, omegay))
previous_pose = current_pose
current_pose = update_current_pose(current_pose, vx, vz,
omegay)
## compute new_state
current_img, current_depth = get_obs(current_pose)
next_left_img, next_left_depth = current_img.copy(
), current_depth.copy()
new_opticalFlow = genGtDenseCorrespondenseFlowMap(
next_left_depth, goal_depth, current_pose,
goal_pose)[:, :, :2]
normalized_next_left_depth = normalize_depth(
next_left_depth)
new_overlapArea = new_opticalFlow * normalized_next_left_depth
new_state = [new_overlapArea]
#assert 1==2
## visualize the state
'''
fig = plt.figure(figsize=(20, 6))
r, c, = 1, 4
ax = fig.add_subplot(r, c, 1)
ax.imshow(next_left_img)
ax = fig.add_subplot(r, c, 2)
ax.imshow(goal_img)
ax = fig.add_subplot(r, c, 3)
show_opticalFlow = np.concatenate((normalized_newOpticalFlow, np.zeros((256, 256, 1), dtype=np.float32)), axis=2)
ax.imshow(show_opticalFlow)
ax = fig.add_subplot(r, c, 4)
show_depth = np.squeeze(normalized_next_left_depth, axis=2)
ax.imshow(show_depth)
plt.show()
assert 1==2
'''
## collision done only stops continuing the sequence, but won't affect reward computing
reward, done, collision_done = decide_reward_and_done(
previous_pose, current_pose, goal_pose, start_pose)
print('done = {}, collision_done = {}'.format(
done, collision_done))
if i_step == seq_len - 1:
print('used up all the steps ...')
done = 1
agent.memory.push(state, action, reward, new_state, done)
if len(agent.memory) > batch_size:
agent.update(batch_size)
state = new_state
episode_reward += reward
print(
'---------------- end of a action ------------------ ')
if done or collision_done:
break
print('---------------- end of a sequence ------------------ ')
rewards.append(episode_reward)
avg_rewards.append(np.mean(rewards[-10:]))
sys.stdout.write(
"------------------------------------epoch = {}, point = {}, traj = {}, reward: {}, average_reward: {} #_steps: {}\n"
.format(i_epoch, point_idx, right_img_idx,
np.round(episode_reward, decimals=2),
np.round(avg_rewards[-1], decimals=2), i_step))
if right_img_idx % 10 == 0:
agent.update_critic()
## plot the running_loss
plt.plot(rewards, label='reward')
plt.plot(avg_rewards, label='avg_reward')
plt.xlabel('Episode')
plt.ylabel('Reward')
plt.grid(True)
plt.legend(loc='upper right')
plt.yscale('linear')
plt.title('change of reward and avg_reward')
plt.savefig('{}/Reward_episode_{}_{}.jpg'.format(
model_weights_save_path, num_episodes, scene_name),
bbox_inches='tight')
plt.close()
torch.save(
agent.actor.state_dict(),
'{}/dqn_epoch_200000_{}.pt'.format(
model_weights_save_path, scene_name))
torch.save(
agent.actor.state_dict(),
'{}/dqn_epoch_200000.pt'.format(
model_weights_save_path))