121 lines
4.5 KiB
Python
121 lines
4.5 KiB
Python
import sys,os
|
|
curr_path = os.path.dirname(__file__)
|
|
parent_path = os.path.dirname(curr_path)
|
|
sys.path.append(parent_path) # add current terminal path to sys.path
|
|
|
|
|
|
import gym
|
|
import numpy as np
|
|
import torch
|
|
import torch.optim as optim
|
|
import datetime
|
|
from common.multiprocessing_env import SubprocVecEnv
|
|
from A2C.model import ActorCritic
|
|
from common.utils import save_results, make_dir
|
|
from common.plot import plot_rewards
|
|
|
|
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
|
|
class A2CConfig:
|
|
def __init__(self) -> None:
|
|
self.algo='A2C'
|
|
self.env= 'CartPole-v0'
|
|
self.result_path = curr_path+"/outputs/" +self.env+'/'+curr_time+'/results/' # path to save results
|
|
self.model_path = curr_path+"/outputs/" +self.env+'/'+curr_time+'/models/' # path to save models
|
|
self.n_envs = 8
|
|
self.gamma = 0.99
|
|
self.hidden_size = 256
|
|
self.lr = 1e-3 # learning rate
|
|
self.max_frames = 30000
|
|
self.n_steps = 5
|
|
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
|
def make_envs(env_name):
|
|
def _thunk():
|
|
env = gym.make(env_name)
|
|
env.seed(2)
|
|
return env
|
|
return _thunk
|
|
def test_env(env,model,vis=False):
|
|
state = env.reset()
|
|
if vis: env.render()
|
|
done = False
|
|
total_reward = 0
|
|
while not done:
|
|
state = torch.FloatTensor(state).unsqueeze(0).to(cfg.device)
|
|
dist, _ = model(state)
|
|
next_state, reward, done, _ = env.step(dist.sample().cpu().numpy()[0])
|
|
state = next_state
|
|
if vis: env.render()
|
|
total_reward += reward
|
|
return total_reward
|
|
def compute_returns(next_value, rewards, masks, gamma=0.99):
|
|
R = next_value
|
|
returns = []
|
|
for step in reversed(range(len(rewards))):
|
|
R = rewards[step] + gamma * R * masks[step]
|
|
returns.insert(0, R)
|
|
return returns
|
|
|
|
|
|
def train(cfg,envs):
|
|
env = gym.make(cfg.env) # a single env
|
|
env.seed(10)
|
|
state_dim = envs.observation_space.shape[0]
|
|
action_dim = envs.action_space.n
|
|
model = ActorCritic(state_dim, action_dim, cfg.hidden_size).to(cfg.device)
|
|
optimizer = optim.Adam(model.parameters())
|
|
frame_idx = 0
|
|
test_rewards = []
|
|
test_ma_rewards = []
|
|
state = envs.reset()
|
|
while frame_idx < cfg.max_frames:
|
|
log_probs = []
|
|
values = []
|
|
rewards = []
|
|
masks = []
|
|
entropy = 0
|
|
# rollout trajectory
|
|
for _ in range(cfg.n_steps):
|
|
state = torch.FloatTensor(state).to(cfg.device)
|
|
dist, value = model(state)
|
|
action = dist.sample()
|
|
next_state, reward, done, _ = envs.step(action.cpu().numpy())
|
|
log_prob = dist.log_prob(action)
|
|
entropy += dist.entropy().mean()
|
|
log_probs.append(log_prob)
|
|
values.append(value)
|
|
rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(cfg.device))
|
|
masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(cfg.device))
|
|
state = next_state
|
|
frame_idx += 1
|
|
if frame_idx % 100 == 0:
|
|
test_reward = np.mean([test_env(env,model) for _ in range(10)])
|
|
print(f"frame_idx:{frame_idx}, test_reward:{test_reward}")
|
|
test_rewards.append(test_reward)
|
|
if test_ma_rewards:
|
|
test_ma_rewards.append(0.9*test_ma_rewards[-1]+0.1*test_reward)
|
|
else:
|
|
test_ma_rewards.append(test_reward)
|
|
# plot(frame_idx, test_rewards)
|
|
next_state = torch.FloatTensor(next_state).to(cfg.device)
|
|
_, next_value = model(next_state)
|
|
returns = compute_returns(next_value, rewards, masks)
|
|
log_probs = torch.cat(log_probs)
|
|
returns = torch.cat(returns).detach()
|
|
values = torch.cat(values)
|
|
advantage = returns - values
|
|
actor_loss = -(log_probs * advantage.detach()).mean()
|
|
critic_loss = advantage.pow(2).mean()
|
|
loss = actor_loss + 0.5 * critic_loss - 0.001 * entropy
|
|
optimizer.zero_grad()
|
|
loss.backward()
|
|
optimizer.step()
|
|
return test_rewards, test_ma_rewards
|
|
if __name__ == "__main__":
|
|
cfg = A2CConfig()
|
|
envs = [make_envs(cfg.env) for i in range(cfg.n_envs)]
|
|
envs = SubprocVecEnv(envs) # 8 env
|
|
rewards,ma_rewards = train(cfg,envs)
|
|
make_dir(cfg.result_path,cfg.model_path)
|
|
save_results(rewards,ma_rewards,tag='train',path=cfg.result_path)
|
|
plot_rewards(rewards,ma_rewards,tag="train",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
|