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REINFORCE
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import argparse | |
import gym | |
import os | |
import torch | |
import torch.nn as nn | |
import torch.optim as optim | |
import torch.nn.functional as F | |
import seaborn as sns | |
import pandas as pd | |
import numpy as np | |
from gym import wrappers, logger | |
sns.set(style="darkgrid") | |
class PGActor(nn.Module): | |
def __init__(self, lr, input_dims, fc1_dims, fc2_dims, n_actions): | |
super(PGActor, self).__init__() | |
self.input_dims = input_dims | |
self.fc1_dims = fc1_dims | |
self.fc2_dims = fc2_dims | |
self.n_actions = n_actions | |
self.fc1 = nn.Linear(self.input_dims, self.fc1_dims) | |
self.fc2 = nn.Linear(self.fc1_dims, self.fc2_dims) | |
self.fc3 = nn.Linear(self.fc2_dims, self.n_actions) | |
self.optimizer = optim.Adam(self.parameters(), lr=lr) | |
self.device = torch.device( | |
'cuda:0' if torch.cuda.is_available() else 'cpu') | |
self.to(self.device) | |
def forward(self, observation): | |
x = torch.Tensor(observation).to(self.device) | |
x = F.relu(self.fc1(x)) | |
x = F.relu(self.fc2(x)) | |
out = self.fc3(x) | |
return out | |
class PGAgent(object): | |
def __init__(self, lr, input_dims, n_actions, agent_name, gamma=0.99, | |
fc1_dims=128, fc2_dims=256, episode=0): | |
self.reward_memory = [] | |
self.action_memory = [] | |
self.score_history = [] # episode history for plot | |
self.gamma = gamma # discount factor | |
self.cur_episode = episode | |
self.agent_name = f"PG_{agent_name}" | |
self.actor = PGActor(lr, input_dims, fc1_dims, fc2_dims, | |
n_actions) | |
def __str__(self): | |
return self.agent_name | |
def predict(self, observation): | |
probabilities = F.softmax(self.actor.forward(observation), dim=-1) | |
action_probs = torch.distributions.Categorical(probabilities) | |
action = action_probs.sample() | |
log_probs = action_probs.log_prob(action) | |
self.action_memory.append(log_probs) | |
return action.item() | |
def store_rewards(self, reward): | |
self.reward_memory.append(reward) | |
def clear_memory(self): | |
self.action_memory = [] | |
self.reward_memory = [] | |
def choose_action(self, observation): | |
_, action_t = torch.max(self.actor.forward(observation), dim=-1) | |
return action_t.cpu().item() | |
def save_model(self, path, episode): | |
torch.save({ | |
'model_state_dict': self.actor.state_dict(), | |
'optimizer_state_dict': self.actor.optimizer.state_dict(), | |
'cur_episode': episode | |
}, path) | |
def load_model(self, path, test=False): | |
checkpoint = torch.load(path) | |
self.actor.load_state_dict(checkpoint['model_state_dict']) | |
self.actor.optimizer.load_state_dict( | |
checkpoint['optimizer_state_dict']) | |
self.cur_episode = checkpoint['cur_episode'] | |
if test: | |
self.actor.eval() | |
else: | |
self.actor.train() | |
def plot_curve(self): | |
df = pd.DataFrame(dict(episode=np.arange(len(self.score_history)), | |
score=self.score_history)) | |
sns_plot = sns.relplot( | |
x="episode", | |
y="score", | |
kind="line", | |
data=df) | |
sns_plot.savefig(f"{self.agent_name}.png") | |
logger.info(f" == training curve {self.agent_name} saved") | |
def learn(self): | |
self.actor.optimizer.zero_grad() | |
# Calcualte discount reward G[] | |
cumulate_reward = 0 | |
G = np.zeros_like(self.reward_memory, dtype=np.float64) | |
for idx in reversed(range(len(self.reward_memory))): | |
if self.reward_memory[idx] != 0: | |
cumulate_reward = cumulate_reward * \ | |
self.gamma + self.reward_memory[idx] | |
G[idx] = cumulate_reward | |
# Normalize | |
mean = np.mean(G) | |
std = np.std(G) if np.std(G) > 0 else 1 | |
G = (G - mean) / std | |
loss = 0 | |
for g, logprob in zip(G, self.action_memory): | |
loss += -g * logprob | |
loss.backward() | |
self.actor.optimizer.step() | |
self.clear_memory() | |
def train(self, env, episodes): | |
max_score = -10086 | |
for eps in range(self.cur_episode, episodes): | |
ob = env.reset() | |
score = 0 | |
done = False | |
episode_step = 0 | |
while not done: | |
action = self.predict(ob) | |
ob, reward, done, _ = env.step(action) | |
self.store_rewards(reward) | |
score += reward | |
episode_step += 1 | |
self.score_history.append(score) | |
max_score = score if score > max_score else max_score | |
if score > -1.0 * episode_step: | |
self.learn() | |
logger.info( | |
f" == episode: {eps+1}, score: {score}, max score: {max_score}") | |
else: | |
self.clear_memory() | |
if (eps + 1) % 100 == 0: | |
ckpt_name = os.path.join(ckpt_save_path, f"ckpt_{eps}.pth") | |
self.save_model(ckpt_name, eps) | |
logger.info(f" == model {ckpt_name} saved") | |
ckpt_name = os.path.join(ckpt_save_path, "ckpt_final.pth") | |
self.save_model(ckpt_name, eps) | |
logger.info(f" == model {ckpt_name} saved") | |
self.plot_curve() | |
def test(self): | |
ob = env.reset() | |
with torch.no_grad(): | |
score = 0 | |
done = False | |
while not done: | |
action = self.predict(ob) | |
ob, reward, done, _ = env.step(action) | |
score += reward | |
logger.info(f" == final score: {score}") | |
if __name__ == '__main__': | |
parser = argparse.ArgumentParser( | |
description="Reinforcement Learning Algorithm for OpenAI Gym Benchmark") | |
parser.add_argument('--env_name', type=str, default='CartPole-v1', | |
help='Select the environment to run') | |
parser.add_argument('--output_path', type=str, default=os.getcwd(), | |
help='Output path for saving records or models') | |
parser.add_argument('--mode', default='train', type=str, | |
help='Optional: [train, resume, test]') | |
parser.add_argument('--env_seed', type=int, default=0, | |
help='Seed for environment') | |
parser.add_argument('--episodes', type=int, default=1000, | |
help='Episode for training') | |
parser.add_argument('--lr', type=float, default=5e-4, | |
help='Learning rate for training') | |
parser.add_argument('--checkpoint', default='', | |
help='Checkpoint for resume or testing') | |
parser.add_argument('--save_record', action='store_true', | |
help='Save record or not') | |
args = parser.parse_args() | |
logger.set_level(logger.INFO) | |
record_save_path = os.path.join(args.output_path, "records") | |
ckpt_save_path = os.path.join(args.output_path, "checkpoint") | |
os.makedirs(record_save_path, exist_ok=True) | |
os.makedirs(ckpt_save_path, exist_ok=True) | |
env = gym.make(args.env_name) | |
# seed for reproducible random numbers | |
if args.env_seed: | |
env.seed(args.env_seed) | |
assert isinstance( | |
env.action_space, gym.spaces.discrete.Discrete | |
), f"REINFORCE is only for discrete task" | |
total_actions = env.action_space.n | |
input_dims = env.observation_space.shape[0] | |
logger.info(f" == action space: {env.action_space}") | |
logger.info(f" == observation space: {env.observation_space}") | |
if args.save_record: | |
env = wrappers.Monitor( | |
env, | |
directory=record_save_path, | |
video_callable=lambda count: (count) % 100 == 0, | |
force=True | |
) | |
agent = PGAgent( | |
lr=args.lr, | |
input_dims=input_dims, | |
n_actions=total_actions, | |
agent_name=args.env_name, | |
gamma=0.99, | |
fc1_dims=128, | |
fc2_dims=256 | |
) | |
if args.mode == "resume": | |
agent.load_model(args.checkpoint) | |
logger.info(f" == model {args.checkpoint} loaded, continue to train") | |
agent.train(env, args.episodes) | |
elif args.mode == "test": | |
agent.load_model(args.checkpoint, test=True) | |
logger.info(f" == model {args.checkpoint} loaded, start to test") | |
agent.test() | |
else: | |
logger.info(f" == start to train from scratch") | |
agent.train(env, args.episodes) | |
# close the env and write monitor result info to disk | |
env.close() |
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