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import argparse
from collections import deque
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical
from ignite.engine import Engine, Events
try:
import gymnasium as gym
except ImportError:
raise ModuleNotFoundError("Please install opengym: pip install gymnasium")
eps = np.finfo(np.float32).eps.item()
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.affine1 = nn.Linear(4, 128)
self.dropout = nn.Dropout(p=0.6)
self.affine2 = nn.Linear(128, 2)
self.saved_log_probs = []
self.rewards = []
def forward(self, x):
x = self.affine1(x)
x = self.dropout(x)
x = F.relu(x)
action_scores = self.affine2(x)
return F.softmax(action_scores, dim=1)
def select_action(policy, observation):
state = torch.from_numpy(observation).float().unsqueeze(0)
probs = policy(state)
m = Categorical(probs)
action = m.sample()
policy.saved_log_probs.append(m.log_prob(action))
return action.item()
def finish_episode(policy, optimizer, gamma):
R = 0
policy_loss = []
returns = deque()
for r in policy.rewards[::-1]:
R = r + gamma * R
returns.appendleft(R)
returns = torch.tensor(returns)
returns = (returns - returns.mean()) / (returns.std() + eps)
for log_prob, R in zip(policy.saved_log_probs, returns):
policy_loss.append(-log_prob * R)
optimizer.zero_grad()
policy_loss = torch.cat(policy_loss).sum()
policy_loss.backward()
optimizer.step()
del policy.rewards[:]
del policy.saved_log_probs[:]
EPISODE_STARTED = Events.EPOCH_STARTED
EPISODE_COMPLETED = Events.EPOCH_COMPLETED
def main(env, args):
policy = Policy()
optimizer = optim.Adam(policy.parameters(), lr=1e-2)
timesteps = range(10000)
def run_single_timestep(engine, timestep):
observation = engine.state.observation
action = select_action(policy, observation)
engine.state.observation, reward, done, _, _ = env.step(action)
if args.render:
env.render()
policy.rewards.append(reward)
engine.state.ep_reward += reward
if done:
engine.terminate_epoch()
engine.state.timestep = timestep
trainer = Engine(run_single_timestep)
trainer.state.running_reward = 10
@trainer.on(EPISODE_STARTED)
def reset_environment_state():
torch.manual_seed(args.seed + trainer.state.epoch)
trainer.state.observation, _ = env.reset(seed=args.seed + trainer.state.epoch)
trainer.state.ep_reward = 0
@trainer.on(EPISODE_COMPLETED)
def update_model():
trainer.state.running_reward = 0.05 * trainer.state.ep_reward + (1 - 0.05) * trainer.state.running_reward
finish_episode(policy, optimizer, args.gamma)
@trainer.on(EPISODE_COMPLETED(every=args.log_interval))
def log_episode():
i_episode = trainer.state.epoch
print(
f"Episode {i_episode}\tLast reward: {trainer.state.ep_reward:.2f}"
f"\tAverage length: {trainer.state.running_reward:.2f}"
)
@trainer.on(EPISODE_COMPLETED)
def should_finish_training():
running_reward = trainer.state.running_reward
if running_reward > env.spec.reward_threshold:
print(
f"Solved! Running reward is now {running_reward} and "
f"the last episode runs to {trainer.state.timestep} time steps!"
)
trainer.should_terminate = True
trainer.run(timesteps, max_epochs=args.max_episodes)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="PyTorch REINFORCE example")
parser.add_argument("--gamma", type=float, default=0.99, metavar="G", help="discount factor (default: 0.99)")
parser.add_argument("--seed", type=int, default=543, metavar="N", help="random seed (default: 543)")
parser.add_argument("--render", action="store_true", help="render the environment")
parser.add_argument(
"--log-interval", type=int, default=10, metavar="N", help="interval between training status logs (default: 10)"
)
parser.add_argument(
"--max-episodes",
type=int,
default=1000000,
metavar="N",
help="Number of episodes for the training (default: 1000000)",
)
args = parser.parse_args()
env = gym.make("CartPole-v1")
main(env, args)
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