# Doesn't official REINFORCE example work?

I was trying to re-implement the Cliff Walking game in Sutton’s book. A simple way is to transfer the official REINFORCE example to this game. Therefore, I directly apply the algorithm given in https://github.com/pytorch/examples/edit/master/reinforcement_learning/reinforce.py. However, it seems that the model doesn’t learn well in this simple setting. I was just wondering where goes wrong?

``````import argparse
import numpy as np
from itertools import count

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical

class CliffWalking(object):
def __init__(self, step_limit=None):
self.shape = (4, 12)

# always start from the left-dow corner
# self.pos = np.asarray([self.shape[0] - 1, 0])
self.pos = np.asarray([2, 11])

# build a
self.cliff = np.zeros(self.shape, dtype=np.bool)
self.cliff[-1, 1:-1] = 1

self.actions = {
'U': 0,
'D': 1,
'L': 2,
'R': 3
}

self.action2shift = {
0: [-1, 0],
1: [1, 0],
2: [0, -1],
3: [0, 1]
}

self.transmit_tensor = self._build_transmit_tensor_()

self.num_actions = len(self.actions)
self.state_dim = 2

def _build_transmit_tensor_(self):
trans_matrix = [[[] for _ in range(self.shape[1])] for __ in range(self.shape[0])]
for i in range(self.shape[0]):
for j in range(self.shape[1]):
for a in range(len(self.actions)):
trans_matrix[i][j].append(self._cal_new_position_((i, j), a))

return trans_matrix

def _cal_new_position_(self, old_pos, action):
old_pos = np.asarray(old_pos)
new_pos = old_pos + self.action2shift[action]
new_pos[0] = max(new_pos[0], 0)
new_pos[0] = min(new_pos[0], self.shape[0] - 1)
new_pos[1] = max(new_pos[1], 0)
new_pos[1] = min(new_pos[1], self.shape[1] - 1)

reward = -1.
terminate = False

if self.cliff[old_pos[0]][old_pos[1]]:
reward = -100.
new_pos[0] = self.shape[0] - 1
new_pos[1] = 0
terminate = True

if old_pos[0] == self.shape[0] - 1 and old_pos[1] == self.shape[1] - 1:
terminate = True
new_pos[0] = self.shape[0] - 1
new_pos[1] = self.shape[1] - 1

return new_pos, reward, terminate

def take_action(self, action):
if isinstance(action, str):
new_pos, reward, terminate = self.transmit_tensor[self.pos[0]][self.pos[1]][self.actions[action]]
else:
new_pos, reward, terminate = self.transmit_tensor[self.pos[0]][self.pos[1]][action]
self.pos[0] = new_pos[0]
self.pos[1] = new_pos[1]
self.steps += 1
return new_pos, reward, terminate

def show_pos(self):
env = np.zeros(self.shape)
env[self.pos[0]][self.pos[1]] = 1
print(env)

def reset(self):
# self.pos = np.asarray([self.shape[0] - 1, 0])
self.pos = np.asarray([2, 10])
self.steps = 0
return self.pos

parser = argparse.ArgumentParser(description='PyTorch REINFORCE example')
help='discount factor (default: 0.99)')
help='random seed (default: 543)')
help='render the environment')
help='interval between training status logs (default: 10)')
args = parser.parse_args()

env = CliffWalking()
torch.manual_seed(args.seed)

class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.affine1 = nn.Linear(2, 48)
self.affine2 = nn.Linear(48, 4)

self.saved_log_probs = []
self.rewards = []

def forward(self, x):
x = F.relu(self.affine1(x))
action_scores = self.affine2(x)
return F.softmax(action_scores, dim=1)

policy = Policy()
eps = np.finfo(np.float32).eps.item()

def select_action(state):
state = torch.from_numpy(state).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():
R = 0
policy_loss = []
rewards = []
for r in policy.rewards[::-1]:
R = r + args.gamma * R
rewards.insert(0, R)
rewards = torch.tensor(rewards)
rewards = (rewards - rewards.mean()) / (rewards.std() + eps)
for log_prob, reward in zip(policy.saved_log_probs, rewards):
policy_loss.append(-log_prob * reward)
policy_loss = torch.cat(policy_loss).sum()
policy_loss.backward()
optimizer.step()
del policy.rewards[:]
del policy.saved_log_probs[:]

def main():
for i_episode in count(1):
state = env.reset()
done = False
while not done:  # Don't infinite loop while learning
action = select_action(state)
state, reward, done = env.take_action(action)

policy.rewards.append(reward)

finish_episode()

if i_episode % 10 == 0 and (not i_episode == 0):
scores = []
for i in range(100):
terminate = False
state = env.reset()
score = 0
while not terminate:
action = select_action(state)
next_state, reward, terminate = env.take_action(action)
score += reward
scores.append(score)
print('[test score]episode %d: %.2f' % (i_episode, np.mean(np.asarray(scores))))

if __name__ == '__main__':
main()

``````

That’s an interesting problem. Of course, the algorithm REINFORCE as implemented in the official example does work on the cliff game. But your code will never learn anything for two reasons:

1. Your implementation of the cliff is not 100% exactly the game from the book (and quite a complex implementation for a simple grid-world).

2. The model (not the algorithm) you are using is not adapted to the type of inputs.

First, in the cliff game, the only terminal state is the goal. If you reach the cliff, you have a transition that leads you back to the starting point with reward -100. This detail is important, because if you break episodes at cliffs, you may never explore enough and quickly learn to stay around the start point. Here is a simple way to implement the true cliff game as a gym environment:

``````class CliffWalking(object):
def __init__(self):
self.R = -np.ones((4, 12))
self.R[-1, -1] = 0
self.R[-1, 1:-1] = -100
print(self.R)

def reset(self):
self.x = 3
self.y = 0
obs = self.x + 4*self.y
return obs

def step(self, action):
if action==0 and self.x<3:
self.x += 1
if action==1 and self.x>0:
self.x -= 1
if action==2 and self.y<11:
self.y += 1
if action==3 and self.y>0:
self.y -= 1

# get reward:
reward = self.R[self.x, self.y]

done = False
# if goal, done= True:
if self.x==3 and self.y==11:
done = True

# reset if goal or cliff:
if self.x==3:
self.y = 0

obs = self.x + 4*self.y
info = (action, self.x, self.y, reward)
return obs, reward, done, info
``````

Now, the inputs (states) are a position in the grid (in your code, it’s x-y coordinate, in my it’s simply x + 4y, the coordinate in a flat version of the grid). For a neural network, such integer inputs are impossible to handle. A neural network expects floating values, for ex between -1 and 1 or between 0 and 1. One solution would be to use an embedding: for ex, take a vector of length 12*4 with zero everywhere but a one at your position in the grid. But that’s very complicated for nothing and very hard to learn because of the number of parameters. A much simpler solution is to use a tabular model instead of a neural network:

``````class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.Q = nn.Parameter(torch.zeros(1, 4*12, 4))
self.saved_log_probs = []
self.rewards = []

def forward(self, x):
action_scores = self.Q[:,x,:]
return F.softmax(action_scores, dim=1)
``````

Then, you can chose long-enough episodes (1000 steps) so you ensure that your algorithm will reach the goal by chance at least once:

``````def main():
for i_episode in range(500):
state = env.reset()
sum_reward = 0
for t in range(1000):
action = select_action(state, i_episode)
state, reward, done, info = env.step(action)
sum_reward += reward
if args.render:
env.render()
policy.rewards.append(reward)
if done:
break

running_reward = sum_reward
finish_episode()
if i_episode % args.log_interval == 0:
print('Episode {}\tSum rewards: {:.2f}'.format(
i_episode, running_reward))
``````

Now it should work quite well, and using good learning rate (0.5 with Adam descent) it learns in ~ 100 episodes a near-optimal solution using 16 action-steps (the opimal is 13, but is “unsafe”, as described in the book).

1 Like

Thanks so much! I’d like to explain why I change the game rule. It is because that one episode may take too long to terminate. As I want to avoid too long episode, I added another another rule. But, you’re right, the original version doesn’t terminate an episode after walking down the cliff.

Your suggestion on one-hot representation of state is really really helpful! Thanks so much for this, I ignored this detail.

If you don’t mind, I would like to discuss the code you gave. I implement the tabular-based TD method in my repo here. Both of Q-learning and Sarsa work fine. The only reason I use NN is want to implement simple policy gradient methods first and then move on to deep RL.

Thanks so much for your help!!!

I just knew that “native replacement of linear model with neural networks would suffer from high correlation between consecutive experiences”. Although I didn’t fully understand this problem, it seems that it is what caused my problem.