def main():
#Parse arguments
#----------------------------
parser = argparse.ArgumentParser()
parser.add_argument("--env", default="CartPole-v0")
parser.add_argument("--conti", action="store_true")
parser.add_argument("--unwrap", action="store_true")
args = parser.parse_args()
#Parameters
#----------------------------
env_id = args.env
save_dir = "./save"
device = "cuda:0"
#Create environment
#----------------------------
env = gym.make(env_id)
if args.conti:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.shape[0]
else:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.n
if args.unwrap:
env = env.unwrapped
#Create model
#----------------------------
policy_net = PolicyNet(s_dim, a_dim, conti=args.conti).to(device)
#Load model
#----------------------------
if os.path.exists(os.path.join(save_dir, "{}.pt".format(env_id))):
print("Loading the model ... ", end="")
checkpoint = torch.load(os.path.join(save_dir, "{}.pt".format(env_id)))
policy_net.load_state_dict(checkpoint["PolicyNet"])
print("Done.")
else:
print("Error: No model saved")
#Start playing
#----------------------------
policy_net.eval()
for it in range(100):
ob = env.reset()
ret = 0
while True:
env.render()
action = policy_net.action_step(torch.from_numpy(np.expand_dims(ob.__array__(), axis=0)).float().to(device), deterministic=True)
ob, reward, done, info = env.step(action.cpu().detach().numpy()[0])
ret += reward
if done:
print("return = {:.4f}".format(ret))
break
env.close()
def main():
#Parse arguments
#----------------------------
parser = argparse.ArgumentParser()
parser.add_argument("--env", default="CartPole-v0")
parser.add_argument("--conti", action="store_true")
parser.add_argument("--render", action="store_true")
parser.add_argument("--unwrap", action="store_true")
parser.add_argument("--episode", default=1000)
args = parser.parse_args()
#Parameters
#----------------------------
env_id = args.env
save_dir = "./save"
device = "cuda:0"
n_episode = args.episode
#Create environment
#----------------------------
env = gym.make(env_id)
if args.conti:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.shape[0]
else:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.n
if args.unwrap:
env = env.unwrapped
#Create model
#----------------------------
policy_net = PolicyNet(s_dim, a_dim, conti=args.conti).to(device)
#Load model
#----------------------------
if os.path.exists(os.path.join(save_dir, "{}.pt".format(env_id))):
print("Loading the model ... ", end="")
checkpoint = torch.load(os.path.join(save_dir, "{}.pt".format(env_id)))
policy_net.load_state_dict(checkpoint["PolicyNet"])
print("Done.")
else:
print("Error: No model saved")
#Start playing
#----------------------------
policy_net.eval()
s_traj = []
a_traj = []
for i_episode in range(n_episode):
ob = env.reset()
ret = 0
s_traj.append([])
a_traj.append([])
while True:
if args.render:
env.render()
action = policy_net.action_step(torch.FloatTensor(np.expand_dims(ob, axis=0)).to(device), deterministic=True)
action = action.cpu().detach().numpy()[0]
s_traj[i_episode].append(ob)
a_traj[i_episode].append(action)
ob, reward, done, info = env.step(action)
ret += reward
if done:
s_traj[i_episode] = np.array(s_traj[i_episode], dtype=np.float32)
if args.conti:
a_traj[i_episode] = np.array(a_traj[i_episode], dtype=np.float32)
else:
a_traj[i_episode] = np.array(a_traj[i_episode], dtype=np.int32)
print("{:d}: return = {:.4f}, len = {:d}".format(i_episode, ret, len(s_traj[i_episode])))
break
#s_traj: (n_episode, timesteps, s_dim)
#a_traj: (n_episode, timesteps, a_dim) or (n_episode, timesteps)
print("Saving the trajectories ... ", end="")
pkl.dump((s_traj, a_traj), open(os.path.join(save_dir, "{}_traj.pkl".format(env_id)), "wb"))
print("Done.")
env.close()
def main():
#Parse arguments
#----------------------------
parser = argparse.ArgumentParser()
parser.add_argument("--env", default="BipedalWalker-v3")
parser.add_argument("--discrete", action="store_true")
parser.add_argument("--unwrap", action="store_true")
args = parser.parse_args()
#Parameters
#----------------------------
save_dir = "./save"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
#Create environment
#----------------------------
env = gym.make(args.env)
if args.discrete:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.n
else:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.shape[0]
if args.unwrap:
env = env.unwrapped
#Create model
#----------------------------
policy_net = PolicyNet(s_dim, a_dim, conti=not args.discrete).to(device)
print(policy_net)
#Load model
#----------------------------
model_path = os.path.join(save_dir, "{}.pt".format(args.env))
if os.path.exists(model_path):
print("Loading the model ... ", end="")
checkpoint = torch.load(model_path)
policy_net.load_state_dict(checkpoint["PolicyNet"])
start_it = checkpoint["it"]
print("Done.")
else:
print("Error: No model saved")
os.exit(1)
#Start training
#----------------------------
policy_net.eval()
with torch.no_grad():
for it in range(10):
ob = env.reset()
total_reward = 0
length = 0
while True:
env.render()
ob_tensor = torch.tensor(np.expand_dims(ob, axis=0), dtype=torch.float32, device=device)
action = policy_net.action_step(ob_tensor, deterministic=True).cpu().numpy()
ob, reward, done, info = env.step(action[0])
total_reward += reward
length += 1
if done:
print("Total reward = {:.6f}, length = {:d}".format(total_reward, length))
break
env.close()