class RDQN(object):
# Initialize Buffer, Networks, global variables
# and configure CUDA
def __init__(self,
env,
buffer_size=10000,
active_cuda=True,
nb_episodes=2000,
max_steps=3500,
discount_factor=0.995,
epsilon_greedy_end=0.01,
epsilon_greedy_start=0.1,
batch_size=128,
update_target=10,
env_type="Unity",
train=True,
save_episode=800,
skip_frame=4,
stack_size=4,
nb_episodes_decay=100,
save_path="gym_cartpole",
nb_action=2,
lr=0.002,
weight_decay=1e-6,
update_plot=10,
rgb=False,
seq_len=8,
nb_samples_episodes=4):
# Global parameters
self.env = env
self.nb_episodes = nb_episodes
self.max_steps = max_steps
self.discount_factor = discount_factor
self.batch_size = batch_size
self.update_target = update_target
self.env_type = env_type
self.save_episode = save_episode
self.skip_frame = skip_frame
self.save_path = save_path
self.nb_episodes_decay = nb_episodes_decay
self.nb_action = nb_action
self.lr = lr
self.weight_decay = weight_decay
self.buffer_size = buffer_size
self.update_plot = update_plot
self.nb_channel = 3 if rgb else 1
self.epsilon_greedy_start = epsilon_greedy_start
self.epsilon_greedy_end = epsilon_greedy_end
self.seq_len = seq_len
self.episode_iterator = 0
self.nb_samples_episodes = nb_samples_episodes
# Log to see improvment
self.log_cumulative_reward = []
self.log_loss = []
#################### PSEUDO CODE STEPS ############################
# Initialize replay memory D
self.buffer = Episode_Buffer(self.buffer_size, self.seq_len)
# Initialize Q policy network and Q target network
self.Q_policy_net = RDQN_net(self.nb_action)
self.Q_target_net = RDQN_net(self.nb_action)
# Copy policy weight to target weight
self.Q_target_net.load_state_dict(self.Q_policy_net.state_dict())
############### PYTORCH SPECIFIC INITIALIZATION ###################
# Adapt to cuda
self.active_cuda = active_cuda
if active_cuda:
self.Q_policy_net.cuda()
self.Q_target_net.cuda()
self.FloatTensor = torch.cuda.FloatTensor if active_cuda else torch.FloatTensor
self.LongTensor = torch.cuda.LongTensor if active_cuda else torch.LongTensor
self.ByteTensor = torch.cuda.ByteTensor if active_cuda else torch.ByteTensor
self.Tensor = self.FloatTensor
# Use RMSProp DeepMind's parameters
self.optimizer = torch.optim.RMSprop(self.Q_policy_net.parameters(),
lr=self.lr,
weight_decay=self.weight_decay)
# Init class to process each fram (just call gym_screen_processing.get_screen() to have the processed screen)
self.gym_screen_processing = GymScreenProcessing(self.env, active_cuda)
def train_loop(self, retrain=False):
self.update_epislon_greedy()
print("Train")
if (self.episode_iterator >= self.nb_episodes):
if (not retrain):
"Please pass retrain parameter if you want to retrain the model. Warning: You will loose everything if " \
"you choose to retrain your network."
return
for current_episode in range(self.episode_iterator, self.nb_episodes):
self.buffer.new_episode(self.episode_iterator)
cumulative_reward = 0
self.env.reset()
state = self.get_screen()
print("Episode " + str(self.episode_iterator))
hx = None
cx = None
# Initialize sequence s1 and preprocess (We take difference between two next frame)
for t in range(0, self.max_steps):
if (t % self.skip_frame == 0):
# Select epsilon greedy action
action, hx, cx = self.select_action(
Variable(state, volatile=True), hx, cx)
# Process the action to the environment
env_action = self.get_env_action(action)
_, reward, done, _ = self.env.step(env_action)
cumulative_reward += reward
reward = self.Tensor([reward])
next_state = self.get_screen()
if not done:
not_done_mask = self.ByteTensor(1).fill_(1)
else:
next_state = None
not_done_mask = self.ByteTensor(1).fill_(0)
#reward = self.Tensor([-1])
self.buffer.push(state, action, next_state, reward,
not_done_mask, self.episode_iterator)
self.learn()
state = next_state
if done:
print("Done")
break
else:
self.env.step(env_action)
print("Episode cumulative reward: ")
print(cumulative_reward)
if self.episode_iterator % self.save_episode == 0 and self.episode_iterator != 0:
print("Save parameters checkpoint:")
self.save()
print("End saving")
if self.episode_iterator % self.update_plot == 0:
self.save_plot()
self.episode_iterator += 1
self.update_epislon_greedy()
if current_episode % self.update_target == 0:
self.Q_target_net.load_state_dict(
self.Q_policy_net.state_dict())
self.log_cumulative_reward.append(cumulative_reward)
################################################ LEARNING FUNCTIONS ################################################
# Gradient descent on (yi - Q_target(state))^2
def learn(self):
if (self.buffer.hasAtLeast(self.nb_samples_episodes)):
samples, nb_episodes = self.buffer.sample(self.nb_samples_episodes)
# At least 1 sampled episode
if (nb_episodes > 0):
# Here batches and sequence are mixed like that:
# episode 1 t_1
# episode 2 t_1
# episode.. t_1
# episode n t_1
# episode 1 t_m
# episode 2 t_m
# episode.. t_m
# episode n t_m
[
batch_state, batch_action, batch_reward, batch_next_state,
not_done_batch
] = Transition(*zip(*samples))
batch_state = Variable(torch.cat(batch_state, dim=0))
batch_action = Variable(torch.cat(batch_action))
batch_reward = Variable(torch.cat(batch_reward))
#batch_next_state = Variable(torch.cat(batch_next_state, dim = 0))
not_done_batch = self.ByteTensor(torch.cat(not_done_batch))
non_final_next_states = Variable(torch.cat([
s if s is not None else torch.zeros(1, 1, 84, 84).type(
self.Tensor) for s in batch_next_state
]),
volatile=True)
Q_s_t_a, (_, _) = self.Q_policy_net(batch_state,
batch_size=nb_episodes,
seq_length=self.seq_len)
Q_s_t_a = Q_s_t_a.gather(1, batch_action)
Q_s_next_t_a_result, (_, _) = self.Q_target_net(
non_final_next_states,
batch_size=nb_episodes,
seq_length=self.seq_len)
Q_s_next_t_a = Q_s_next_t_a_result.max(1)[0]
Q_s_next_t_a[1 - not_done_batch] = 0
# Target Q_s_t_a value (like supervised learning )
target_state_value = (Q_s_next_t_a *
self.discount_factor) + batch_reward
target_state_value.detach_()
target_state_value = Variable(
target_state_value.data).unsqueeze_(1)
assert Q_s_t_a.shape == target_state_value.shape
loss = F.smooth_l1_loss(Q_s_t_a, target_state_value)
# Optimize the model
self.optimizer.zero_grad()
loss.backward()
self.log_loss.append(loss.data[0])
for param in self.Q_policy_net.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
def select_action(self, state, hx, cx):
# Greedy action
if (np.random.uniform() > self.epsilon_greedy):
Q_policy_values, (hx, cx) = self.Q_policy_net.forward(state,
hx=hx,
cx=cx)
action = Q_policy_values.data.max(1)[1].view(1, 1)
return action, hx, cx
# Random
else:
return self.LongTensor([[random.randrange(self.nb_action)]
]), hx, cx
# Every episodes
def update_epislon_greedy(self):
self.epsilon_greedy = self.epsilon_greedy_end + (
self.epsilon_greedy_start - self.epsilon_greedy_end) * math.exp(
-1. * self.episode_iterator / self.nb_episodes_decay)
##################################################### SAVE/LOAD FUNCTIONS ##########################################
def save(self):
temp_env = self.env
temp_gym_screen_proc = self.gym_screen_processing
temp_buffer = self.buffer
self.env = None
self.gym_screen_processing = None
self.buffer = None
with open(self.save_path, 'wb') as output:
cPickle.dump(self, output)
self.env = temp_env
self.gym_screen_processing = temp_gym_screen_proc
self.buffer = temp_buffer
def load_env(self, env):
self.env = env
def init_buffer(self):
self.buffer = Buffer(self.buffer_size)
##################################################### ENVIRONMENT TYPE SPECIFIC ####################################
def get_env_action(self, action):
if (self.env_type == "Unity"):
return action.cpu().numpy()
else:
return action.cpu().numpy()[0, 0]
def get_screen(self):
if (self.env_type == "Unity"):
return img_to_tensor(self.env.get_screen())
elif (self.env_type == "Gridworld"):
return img_to_tensor(np.expand_dims(self.env.renderEnv(), axis=3))
#return self.env.renderEnv()
else:
# Gym
return self.gym_screen_processing.get_screen()
#################################################### PLOT SPECIFIC FUNCTIONS #######################################
def save_plot(self):
plt.plot(self.log_cumulative_reward)
plt.title("DRQN on " + self.save_path)
plt.xlabel("Episodes")
plt.ylabel("Cumulative reward")
plt.savefig("../save/" + self.save_path + "_cumulative_rewards.png")
plt.clf()
plt.plot(self.log_loss[100:])
plt.title("DRQN on " + self.save_path)
plt.xlabel("Episodes")
plt.ylabel("Loss")
plt.savefig("../save/" + self.save_path + "_loss.png")
plt.clf()
