def test():
############## Hyperparameters ##############
env_name = 'solved_problem_3'
env = PusherEnv()
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
n_episodes = 1 # num of episodes to run
max_timesteps = 1500 # max timesteps in one episode
render = True # render the environment
save_gif = True # png images are saved in gif folder
# filename and directory to load model from
filename = env_name + ".pth"
directory = "./"
action_std = 0.5 # constant std for action distribution (Multivariate Normal)
K_epochs = 80 # update policy for K epochs
eps_clip = 0.2 # clip parameter for PPO
gamma = 0.99 # discount factor
lr = 0.0003 # parameters for Adam optimizer
betas = (0.9, 0.999)
#############################################
memory = Memory()
ppo = PPO(state_dim, action_dim, action_std, lr, betas, gamma, K_epochs,
eps_clip)
ppo.policy_old.load_state_dict(torch.load(directory + filename))
out = cv2.VideoWriter('./final_eval_p3.mp4',
cv2.VideoWriter_fourcc(*'mp4v'), 30, (640, 480))
robot = env.robot
robot_base = robot.arm.robot_base_pos
robot.cam.setup_camera(focus_pt=robot_base,
dist=3,
yaw=55,
pitch=-30,
roll=0)
for ep in range(1, n_episodes + 1):
ep_reward = 0
state = env.reset()
for t in range(max_timesteps):
action = ppo.select_action(state, memory)
state, reward, done, _ = env.step(action)
ep_reward += reward
if render:
env.render()
if save_gif:
img = robot.cam.get_images(get_rgb=True, get_depth=False)[0]
out.write(np.array(img))
if done:
break
print('Episode: {}\tReward: {}'.format(ep, int(ep_reward)))
ep_reward = 0
env.close()
out.release()
def test():
############## Hyperparameters ##############
env_name = "BipedalWalker-v3"
env = gym.make(env_name)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
n_episodes = 3 # num of episodes to run
max_timesteps = 1500 # max timesteps in one episode
render = True # render the environment
save_gif = False # png images are saved in gif folder
# filename and directory to load model from
# filename = "PPO_continuous_" +env_name+ ".pth"
# filename = "PPO_continuous_BipedalWalker-v2.pth"
# directory = "./preTrained/"
filename = "PPO_continuous_BipedalWalker-v3_1000.pth"
directory = "./saved_models/"
action_std = 0.5 # constant std for action distribution (Multivariate Normal)
K_epochs = 80 # update policy for K epochs
eps_clip = 0.2 # clip parameter for PPO
gamma = 0.99 # discount factor
lr = 0.0003 # parameters for Adam optimizer
betas = (0.9, 0.999)
#############################################
memory = Memory()
ppo = PPO(state_dim, action_dim, action_std, lr, betas, gamma, K_epochs,
eps_clip)
ppo.policy_old.load_state_dict(torch.load(directory + filename))
for ep in range(1, n_episodes + 1):
ep_reward = 0
state = env.reset()
for t in range(max_timesteps):
action = ppo.select_action(state, memory)
state, reward, done, _ = env.step(action)
ep_reward += reward
if render:
env.render()
if save_gif:
img = env.render(mode='rgb_array')
img = Image.fromarray(img)
img.save('./gif/{}.jpg'.format(t))
if done:
break
print('Episode: {}\tReward: {}'.format(ep, int(ep_reward)))
ep_reward = 0
env.close()
def test():
############## Hyperparameters ##############
env_name = "kuka_handlit-v0"
env = gym.make(env_name)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
n_episodes = 200000 # num of episodes to run
max_timesteps = 1500 # max timesteps in one episode
render = True # render the environment
save_gif = False # png images are saved in gif folder
# filename and directory to load model from
path = load_training()
hiddenLayer_neurons = get_structureOf_hiddenLayers()
action_std = 0.5 # constant std for action distribution (Multivariate Normal)
K_epochs = 80 # update policy for K epochs
eps_clip = 0.2 # clip parameter for PPO
gamma = 0.99 # discount factor
lr = 0.0003 # parameters for Adam optimizer
betas = (0.9, 0.999)
#############################################
memory = Memory()
ppo = PPO(state_dim, action_dim, action_std, lr, betas, gamma, K_epochs, eps_clip,hiddenLayer_neurons)
ppo.policy_old.load_state_dict(torch.load(path))
for ep in range(1, n_episodes+1):
ep_reward = 0
state = env.reset()
for t in range(max_timesteps):
action = ppo.select_action(state, memory)
state, reward, done = env.step([action])
ep_reward += reward
if render:
env.render()
if save_gif:
img = env.render(mode = 'rgb_array')
img = Image.fromarray(img)
img.save('./gif/{}.jpg'.format(t))
if done:
break
print('Episode: {}\tReward: {}'.format(ep, int(ep_reward)))
ep_reward = 0
env.close()
def test():
############## Hyperparameters ##############
# env_name = "BipedalWalker-v2"
env_name = 'Duckietown-loop_empty-v0'
# env = gym.make(env_name)
# creating environment
env = launch_env()
# Wrappers
env = ResizeWrapper(env)
env = NormalizeWrapper(env)
env = ImgWrapper(env) # to make the images from 160x120x3 into 3x160x120
env = ActionWrapper(env)
env = DtRewardWrapper(env)
print("Initialized Wrappers")
# state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
state_dim = env.observation_space.shape
state_dim = functools.reduce(operator.mul, state_dim, 1)
max_action = float(env.action_space.high[0])
n_episodes = 3 # num of episodes to run
max_timesteps = 500 # max timesteps in one episode
render = True # render the environment
save_gif = False # png images are saved in gif folder
# filename and directory to load model from
filename = "PPO_continuous_" + env_name + ".pth"
directory = "./preTrained/"
action_std = 0.05 # constant std for action distribution (Multivariate Normal)
K_epochs = 80 # update policy for K epochs
eps_clip = 0.2 # clip parameter for PPO
gamma = 0.99 # discount factor
lr = 0.0003 # parameters for Adam optimizer
betas = (0.9, 0.999)
#############################################
memory = Memory()
ppo = PPO(state_dim, action_dim, action_std, lr, betas, gamma, K_epochs,
eps_clip, max_action, 32)
ppo.policy_old.load_state_dict(
torch.load(directory + filename, map_location=torch.device('cpu')))
for ep in range(1, n_episodes + 1):
ep_reward = 0
state = env.reset()
for t in range(max_timesteps):
action = ppo.select_action(state, memory)
state, reward, done, _ = env.step(action)
ep_reward += reward
if render:
env.render()
if save_gif:
img = env.render(mode='rgb_array')
img = Image.fromarray(img)
img.save('./gif/{}.jpg'.format(t))
if done:
break
print('Episode: {}\tReward: {}'.format(ep, int(ep_reward)))
ep_reward = 0
env.close()