I am implementing simple DQN algorithm using pytorch, to solve the CartPole environment from gym. I have been debugging for a while now, and I cant figure out why the model is not learning.
Observations:
- using
SmoothL1Lossperforms worse thanMSEloss, but loss increases for both - smaller
LRinAdamdoes not work, I have tested using 0.0001, 0.00025, 0.0005 and default
Notes:
- I have debugged various parts of the algorithm individually, and can say with good confidence that the issue is in the
learnfunction. I am wondering if this bug is due to me misunderstandingdetachin pytorch or some other framework mistake im making. - I am trying to stick as close to the original paper as possible (linked above)
References:
import torch as T
import torch.nn as nn
import torch.nn.functional as F
import gym
import numpy as np
class ReplayBuffer:
def __init__(self, mem_size, input_shape, output_shape):
self.mem_counter = 0
self.mem_size = mem_size
self.input_shape = input_shape
self.actions = np.zeros(mem_size)
self.states = np.zeros((mem_size, *input_shape))
self.states_ = np.zeros((mem_size, *input_shape))
self.rewards = np.zeros(mem_size)
self.terminals = np.zeros(mem_size)
def sample(self, batch_size):
indices = np.random.choice(self.mem_size, batch_size)
return self.actions[indices], self.states[indices], \
self.states_[indices], self.rewards[indices], \
self.terminals[indices]
def store(self, action, state, state_, reward, terminal):
index = self.mem_counter % self.mem_size
self.actions[index] = action
self.states[index] = state
self.states_[index] = state_
self.rewards[index] = reward
self.terminals[index] = terminal
self.mem_counter += 1
class DeepQN(nn.Module):
def __init__(self, input_shape, output_shape, hidden_layer_dims):
super(DeepQN, self).__init__()
self.input_shape = input_shape
self.output_shape = output_shape
layers = []
layers.append(nn.Linear(*input_shape, hidden_layer_dims[0]))
for index, dim in enumerate(hidden_layer_dims[1:]):
layers.append(nn.Linear(hidden_layer_dims[index], dim))
layers.append(nn.Linear(hidden_layer_dims[-1], *output_shape))
self.layers = nn.ModuleList(layers)
self.loss = nn.MSELoss()
self.optimizer = T.optim.Adam(self.parameters())
def forward(self, states):
for layer in self.layers[:-1]:
states = F.relu(layer(states))
return self.layers[-1](states)
def learn(self, predictions, targets):
self.optimizer.zero_grad()
loss = self.loss(input=predictions, target=targets)
loss.backward()
self.optimizer.step()
return loss
class Agent:
def __init__(self, epsilon, gamma, input_shape, output_shape):
self.input_shape = input_shape
self.output_shape = output_shape
self.epsilon = epsilon
self.gamma = gamma
self.q_eval = DeepQN(input_shape, output_shape, [64])
self.memory = ReplayBuffer(10000, input_shape, output_shape)
self.batch_size = 32
self.learn_step = 0
def move(self, state):
if np.random.random() < self.epsilon:
return np.random.choice(*self.output_shape)
else:
self.q_eval.eval()
state = T.tensor([state]).float()
action = self.q_eval(state).max(axis=1)[1]
return action.item()
def sample(self):
actions, states, states_, rewards, terminals = \
self.memory.sample(self.batch_size)
actions = T.tensor(actions).long()
states = T.tensor(states).float()
states_ = T.tensor(states_).float()
rewards = T.tensor(rewards).view(self.batch_size).float()
terminals = T.tensor(terminals).view(self.batch_size).long()
return actions, states, states_, rewards, terminals
def learn(self, state, action, state_, reward, done):
self.memory.store(action, state, state_, reward, done)
if self.memory.mem_counter < self.batch_size:
return
self.q_eval.train()
self.learn_step += 1
actions, states, states_, rewards, terminals = self.sample()
indices = np.arange(self.batch_size)
q_eval = self.q_eval(states)[indices, actions]
q_next = self.q_eval(states_).detach()
q_target = rewards + self.gamma * q_next.max(axis=1)[0] * (1 - terminals)
loss = self.q_eval.learn(q_eval, q_target)
self.epsilon *= 0.9 if self.epsilon > 0.1 else 1.0
return loss.item()
def learn(env, agent, episodes=500):
print('Episode: Mean Reward: Last Loss: Mean Step')
rewards = []
losses = [0]
steps = []
num_episodes = episodes
for episode in range(num_episodes):
done = False
state = env.reset()
total_reward = 0
n_steps = 0
while not done:
action = agent.move(state)
state_, reward, done, _ = env.step(action)
loss = agent.learn(state, action, state_, reward, done)
state = state_
total_reward += reward
n_steps += 1
if loss:
losses.append(loss)
rewards.append(total_reward)
steps.append(n_steps)
if episode % (episodes // 10) == 0 and episode != 0:
print(f'{episode:5d} : {np.mean(rewards):5.2f} '
f': {np.mean(losses):5.2f}: {np.mean(steps):5.2f}')
rewards = []
losses = [0]
steps = []
print(f'{episode:5d} : {np.mean(rewards):5.2f} '
f': {np.mean(losses):5.2f}: {np.mean(steps):5.2f}')
return losses, rewards
if __name__ == '__main__':
env = gym.make('CartPole-v1')
agent = Agent(1.0, 1.0,
env.observation_space.shape,
[env.action_space.n])
learn(env, agent, 500)
