I get a “RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation:” when I run the following supposedly simple reinforcement learning code.
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Categorical
import numpy as np
Constants
NUM_ALPHABET = 26
ORDINAL_LOWER_A = ord(‘a’)
Hangman Environment (replace this with your Hangman environment)
class HangmanEnvironment:
def init(self, words):
self.words = words
self.reset()
def reset(self):
self.target_word = np.random.choice(self.words)
self.state = torch.zeros(len(self.target_word), dtype=torch.long)
self.correct_guesses = set()
self.incorrect_guesses = set()
self.attempts = 0
self.max_attempts = 6 # Set the maximum attempts for Hangman
self.game_over = False
def get_state(self):
return self.state
def step(self, action):
if action in self.correct_guesses or action in self.incorrect_guesses:
# Penalize repeated guesses
reward = -1
elif action in self.target_word:
# Correct guess
reward = 1
self.correct_guesses.add(action)
for i, letter in enumerate(self.target_word):
if letter == action:
self.state[i] = 1
else:
# Incorrect guess
reward = -1
self.incorrect_guesses.add(action)
self.attempts += 1
if torch.all(self.state == 1) or self.attempts == self.max_attempts:
# Game over conditions
self.game_over = True
return self.state.clone(), reward, self.game_over
Policy Network
class Policy(nn.Module):
def init(self, vocab_size, embedding_size, hidden_size, action_size):
super(Policy, self).init()
self.embedding = nn.Embedding(vocab_size, embedding_size)
self.rnn = nn.GRU(embedding_size, hidden_size, batch_first=True)
self.fc = nn.Linear(hidden_size, action_size)
def forward(self, input_indices):
embedded_letters = self.embedding(input_indices)
_, hidden_state = self.rnn(embedded_letters)
# You can use the hidden_state as input to the final fully connected layer
x = self.fc(hidden_state.squeeze(0))
return F.softmax(x, dim=1)
def act(self, state):
state = state.unsqueeze(0)
probs = self.forward(state).cpu()
m = Categorical(probs)
action = m.sample()
return action.item(), m.log_prob(action)
Training the Hangman REINFORCE algorithm
def reinforce(env, policy, optimizer, num_episodes=1000, gamma=0.99):
for episode in range(1, num_episodes + 1):
state = env.get_state()
print("printing state ",state)
log_probs =
rewards =
while not env.game_over:
action, log_prob = policy.act(state)
log_probs.append(log_prob)
state, reward, game_over = env.step(chr(action + ORDINAL_LOWER_A))
rewards.append(reward)
# Calculate returns and normalize
returns = []
R = 0
for r in reversed(rewards):
R = r + gamma * R
returns.insert(0, R)
returns = torch.tensor(returns)
returns = (returns - returns.mean()) / (returns.std() + 1e-8)
# Calculate policy loss and update policy
policy_loss = [-log_prob * R for log_prob, R in zip(log_probs, returns)]
policy_loss = torch.cat(policy_loss).sum()
optimizer.zero_grad()
policy_loss.backward()
optimizer.step()
# Print the total reward for each episode
print(f"Episode {episode}, Total Reward: {sum(rewards)}")
# Reset the environment for the next episode
env.reset()
Example usage
if name == “main”:
# Sample words for Hangman
word_list = [“hangman”, “python”, “programming”, “challenge”, “learning”]
# Hangman environment
hangman_env = HangmanEnvironment(word_list)
# Policy network
vocab_size = NUM_ALPHABET + 1 # 26 letters + 1 for "_"
embedding_size = 16
hidden_size = 32
action_size = NUM_ALPHABET
max_word_length = 30
policy_net = Policy(vocab_size, embedding_size, hidden_size, action_size)
# Optimizer
optimizer = optim.Adam(policy_net.parameters(), lr=0.001)
# Train the REINFORCE algorithm
reinforce(hangman_env, policy_net, optimizer, num_episodes=1000)