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May 14, 2020 07:45
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DQN cartpole study
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#!/usr/local/bin/python | |
# Import packages | |
import gym | |
import math | |
import random | |
import numpy as np | |
import matplotlib | |
import matplotlib.pyplot as plt | |
from collections import namedtuple | |
from itertools import count | |
from PIL import Image | |
import torch | |
import torch.nn as nn | |
import torch.optim as optim | |
import torch.nn.functional as F | |
import torchvision.transforms as T | |
# Make cartpole environment | |
env = gym.make('CartPole-v0').unwrapped | |
env.reset() | |
# Set matplotlib | |
is_ipython = 'inline' in matplotlib.get_backend() | |
if is_ipython: | |
from IPython import display | |
plt.ion() | |
# if gpu is to be used | |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
Transition = namedtuple('Transition', | |
('state', 'action', 'next_state', 'reward')) | |
class ReplayMemory(object): | |
def __init__(self, capacity): | |
self.capacity = capacity | |
self.memory = [] | |
self.position = 0 | |
def push(self, *args): | |
"""Saves a transition""" | |
if len(self.memory) < self.capacity: | |
self.memory.append(None) | |
self.memory[self.position] = Transition(*args) | |
self.position = (self.position + 1) % self.capacity | |
def sample(self, batch_size): | |
return random.sample(self.memory, batch_size) | |
def __len__(self): | |
return len(self.memory) | |
class DQN(nn.Module): | |
def __init__(self, h, w, outputs): | |
super(DQN, self).__init__() | |
self.conv1 = nn.Conv2d(3, 16, kernel_size=5, stride=2) | |
self.bn1 = nn.BatchNorm2d(16) | |
self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=2) | |
self.bn2 = nn.BatchNorm2d(32) | |
self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2) | |
self.bn3 = nn.BatchNorm2d(32) | |
# Number of Linear input connections depends on output of conv2d layers | |
# and therefore the input image size, so compute it | |
def conv2d_size_out(size, kernel_size = 5, stride = 2): | |
return (size - (kernel_size - 1) - 1) // stride + 1 | |
convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(w))) | |
convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(h))) | |
linear_input_size = convw * convh * 32 | |
self.head = nn.Linear(linear_input_size, outputs) | |
# Called with either one element to determine next action, or a batch | |
# during optimization. Returns tensor([[left0exp, right0exp]...]). | |
def forward(self, x): | |
x = F.relu(self.bn1(self.conv1(x))) | |
x = F.relu(self.bn2(self.conv2(x))) | |
x = F.relu(self.bn3(self.conv3(x))) | |
return self.head(x.view(x.size(0), -1)) | |
resize = T.Compose([T.ToPILImage(), | |
T.Resize(40, interpolation=Image.CUBIC), | |
T.ToTensor()]) | |
def get_cart_location(screen_width): | |
world_width = env.x_threshold * 2 | |
scale = screen_width / world_width | |
return int(env.state[0] * scale + screen_width / 2.0) # MIDDLE OF CART | |
def get_screen(): | |
# Returned screen requested by gym is 400x600x3, but is sometimes larger | |
# such as 800x1200x3. Transpose it into torch order (CHW). | |
screen = env.render(mode='rgb_array').transpose((2, 0, 1)) | |
# Cart is in the lower half, so strip off the top and bottom of the screen | |
_, screen_height, screen_width = screen.shape | |
screen = screen[:, int(screen_height*0.4):int(screen_height * 0.8)] | |
view_width = int(screen_width * 0.6) | |
cart_location = get_cart_location(screen_width) | |
if cart_location < view_width // 2: | |
slice_range = slice(view_width) | |
elif cart_location > (screen_width - view_width // 2): | |
slice_range = slice(-view_width, None) | |
else: | |
slice_range = slice(cart_location - view_width // 2, | |
cart_location + view_width // 2) | |
# Strip off the edges, so that we have a square image centered on a cart | |
screen = screen[:, :, slice_range] | |
# Convert to float, rescale, convert to torch tensor | |
# (this doesn't require a copy) | |
screen = np.ascontiguousarray(screen, dtype=np.float32) / 255 | |
screen = torch.from_numpy(screen) | |
# Resize, and add a batch dimension (BCHW) | |
return resize(screen).unsqueeze(0).to(device) | |
env.reset() | |
plt.figure() | |
plt.imshow(get_screen().cpu().squeeze(0).permute(1, 2, 0).numpy(), | |
interpolation='none') | |
plt.title('Example extracted screen') | |
plt.show() | |
env.close() | |
BATCH_SIZE = 128 | |
GAMMA = .999 | |
EPS_START = .9 | |
EPS_END = .05 | |
EPS_DECAY = 200 | |
TARGET_UPDATE = 10 | |
# Get screen size so that we can initialize layers correctly based on shape | |
# returned from AI gym. Typical dimensions at this point are close to 3x40x90 | |
# which is the result of a clamped and down-scaled render buffer in get_screen | |
init_screen = get_screen() | |
_, _, screen_height, screen_width = init_screen.shape | |
# Get number of actions from gym action space | |
n_actions = env.action_space.n | |
policy_net = DQN(screen_height, screen_width, n_actions).to(device) | |
target_net = DQN(screen_height, screen_width, n_actions).to(device) | |
target_net.load_state_dict(policy_net.state_dict()) | |
target_net.eval() | |
optimizer = optim.RMSprop(policy_net.parameters()) | |
memory = ReplayMemory(10000) | |
steps_done = 0 | |
def select_action(state): | |
global steps_done | |
sample = random.random() | |
# epsilon value gradually decreases as the state proceeds | |
eps_threshold = EPS_END + (EPS_START - EPS_END) * \ | |
math.exp(-1. * steps_done / EPS_DECAY) | |
steps_done += 1 | |
# epsilon greedy policy | |
if sample > eps_threshold: | |
with torch.no_grad(): | |
# t.max(1) will return largest column value of each row. | |
# second column on max result is index of where max element was | |
# found, so we pick action with the larger expected reward. | |
return policy_net(state).max(1)[1].view(1, 1) | |
else: | |
return torch.tensor([[random.randrange(n_actions)]], device=device, dtype=torch.long) | |
episode_durations = [] | |
def plot_durations(): | |
plt.figure(2) | |
plt.clf() | |
durations_t = torch.tensor(episode_durations, dtype=torch.float) | |
plt.title('Training...') | |
plt.xlabel('Episode') | |
plt.ylabel('Duration') | |
plt.plot(durations_t.numpy()) | |
# Take 100 episode averages and plot them too | |
if len(durations_t) >= 100: | |
means = durations_t.unfold(0, 100, 1).mean(1).view(-1) | |
means = torch.cat((torch.zeros(99), means)) | |
plt.plot(means.numpy()) | |
plt.pause(0.001) # pause a bit so that plots are updated | |
if is_ipython: | |
display.clear_output(wait=True) | |
display.display(plt.gcf()) | |
def optimize_model(): | |
if len(memory) < BATCH_SIZE: | |
return | |
transitions = memory.sample(BATCH_SIZE) | |
# Transpose the batch (see https://stackoverflow.com/a/19343/3343043 for | |
# detailed explanation). This converts batch-array of Transitions | |
# to Transition of batch-arrays. | |
batch = Transition(*zip(*transitions)) | |
# Compute a mask of non-final states and concatenate the batch elements | |
# (a final state would've been the one after which simulation ended) | |
non_final_mask = torch.tensor(tuple(map(lambda s: s is not None, | |
batch.next_state)), device=device, dtype=torch.bool) | |
non_final_next_states = torch.cat([s for s in batch.next_state | |
if s is not None]) | |
state_batch = torch.cat(batch.state) | |
action_batch = torch.cat(batch.action) | |
reward_batch = torch.cat(batch.reward) | |
# Compute Q(s_t, a) - the model computes Q(s_t), then we select the | |
# columns of actions taken. These are the actions which would've been taken | |
# for each batch state according to policy_net | |
state_action_values = policy_net(state_batch).gather(1, action_batch) | |
# Compute V(s_{t+1}) for all next states. | |
# Expected values of actions for non_final_next_states are computed based | |
# on the "older" target_net; selecting their best reward with max(1)[0]. | |
# This is merged based on the mask, such that we'll have either the expected | |
# state value or 0 in case the state was final. | |
next_state_values = torch.zeros(BATCH_SIZE, device=device) | |
next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach() | |
# Compute the expected Q values | |
expected_state_action_values = (next_state_values * GAMMA) + reward_batch | |
# Compute Huber loss | |
loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1)) | |
# Optimize the model | |
optimizer.zero_grad() | |
loss.backward() | |
for param in policy_net.parameters(): | |
param.grad.data.clamp_(-1, 1) | |
optimizer.step() | |
num_episodes = 5000 | |
for i_episode in range(num_episodes): | |
# Initialize the environment and state | |
env.reset() | |
last_screen = get_screen() | |
current_screen = get_screen() | |
state = current_screen - last_screen | |
for t in count(): | |
# Select and perform an action | |
action = select_action(state) | |
_, reward, done, _ = env.step(action.item()) | |
reward = torch.tensor([reward], device=device) | |
# Observe new state | |
last_screen = current_screen | |
current_screen = get_screen() | |
if not done: | |
next_state = current_screen - last_screen | |
else: | |
next_state = None | |
# Store the transition in memory | |
memory.push(state, action, next_state, reward) | |
# Move to the next state | |
state = next_state | |
# Perform one step of the optimization (on the target network) | |
optimize_model() | |
if done: | |
episode_durations.append(t + 1) | |
plot_durations() | |
break | |
# Update the target network, copying all weights and biases in DQN | |
if i_episode % TARGET_UPDATE == 0: | |
target_net.load_state_dict(policy_net.state_dict()) | |
print('Complete') | |
env.render() | |
env.close() | |
plt.ioff() | |
plt.show() |
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