def trainSQL0(file_name="SQL0",
env=GridworldEnv(1),
batch_size=128,
gamma=0.999,
beta=5,
eps_start=0.9,
eps_end=0.05,
eps_decay=1000,
is_plot=False,
num_episodes=200,
max_num_steps_per_episode=1000,
learning_rate=0.0001,
memory_replay_size=10000,
n_step=10,
target_update=10):
"""
Soft Q-learning training routine when observation vector is input
Retuns rewards and durations logs.
"""
num_actions = env.action_space.n
input_size = env.observation_space.shape[0]
model = DQN(input_size, num_actions)
target_model = DQN(input_size, num_actions)
target_model.load_state_dict(model.state_dict())
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# optimizer = optim.RMSprop(model.parameters(), )
use_cuda = torch.cuda.is_available()
if use_cuda:
model.cuda()
memory = ReplayMemory(memory_replay_size, n_step, gamma)
episode_durations = []
mean_durations = []
episode_rewards = []
mean_rewards = []
steps_done, t = 0, 0
for i_episode in range(num_episodes):
if i_episode % 20 == 0:
clear_output()
if i_episode != 0:
print("Cur episode:", i_episode, "steps done:", episode_durations[-1],
"exploration factor:", eps_end + (eps_start - eps_end) * \
math.exp(-1. * steps_done / eps_decay), "reward:", env.episode_total_reward)
# Initialize the environment and state
state = torch.from_numpy(env.reset()).type(torch.FloatTensor).view(
-1, input_size)
for t in count():
# Select and perform an action
action = select_action(state, model, num_actions, eps_start,
eps_end, eps_decay, steps_done)
next_state_tmp, reward, done, _ = env.step(action[0, 0])
reward = Tensor([reward])
# Observe new state
next_state = torch.from_numpy(next_state_tmp).type(
torch.FloatTensor).view(-1, input_size)
if done:
next_state = None
# Store the transition in memory
memory.push(model, target_model, state, action, next_state, reward)
# Move to the next state
state = next_state
# plot_state(state)
# env.render()
# Perform one step of the optimization (on the target network)
optimize_model(model, target_model, optimizer, memory, batch_size,
gamma, beta) #### Difference w.r.t DQN
if done or t + 1 >= max_num_steps_per_episode:
episode_durations.append(t + 1)
episode_rewards.append(
env.episode_total_reward
) ##### Modify for OpenAI envs such as CartPole
if is_plot:
plot_durations(episode_durations, mean_durations)
plot_rewards(episode_rewards, mean_rewards)
steps_done += 1
break
if i_episode % target_update == 0 and i_episode != 0:
target_model.load_state_dict(model.state_dict())
print('Complete')
env.render(close=True)
env.close()
if is_plot:
plt.ioff()
plt.show()
## Store Results
np.save(file_name + '-sql0-rewards', episode_rewards)
np.save(file_name + '-sql0-durations', episode_durations)
return model, episode_rewards, episode_durations
def trainDQN(file_name="DQN",
env=GridworldEnv(1),
batch_size=128,
gamma=0.999,
eps_start=0.9,
eps_end=0.05,
eps_decay=1000,
is_plot=False,
num_episodes=500,
max_num_steps_per_episode=1000,
learning_rate=0.0001,
memory_replay_size=10000):
"""
DQN training routine. Retuns rewards and durations logs.
Plot environment screen
"""
if is_plot:
env.reset()
plt.ion()
plt.figure()
plt.imshow(get_screen(env).cpu().squeeze(0).squeeze(0).numpy(),
interpolation='none')
plt.title("")
plt.draw()
plt.pause(0.00001)
num_actions = env.action_space.n
model = DQN(num_actions)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
use_cuda = torch.cuda.is_available()
if use_cuda:
model.cuda()
memory = ReplayMemory(memory_replay_size)
episode_durations = []
mean_durations = []
episode_rewards = []
mean_rewards = []
steps_done = 0 # total steps
for i_episode in range(num_episodes):
if i_episode % 20 == 0:
clear_output()
print("Cur episode:", i_episode, "steps done:", steps_done,
"exploration factor:", eps_end + (eps_start - eps_end) * \
math.exp(-1. * steps_done / eps_decay))
# Initialize the environment and state
env.reset()
# last_screen = env.current_grid_map
# (1, 1, 8, 8)
current_screen = get_screen(env)
state = current_screen # - last_screen
for t in count():
# Select and perform an action
action = select_action(state, model, num_actions, eps_start,
eps_end, eps_decay, steps_done)
steps_done += 1
_, reward, done, _ = env.step(action[0, 0])
reward = Tensor([reward])
# Observe new state
last_screen = current_screen
current_screen = get_screen(env)
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
# plot_state(state)
# env.render()
# Perform one step of the optimization (on the target network)
optimize_model(model, optimizer, memory, batch_size, gamma)
if done or t + 1 >= max_num_steps_per_episode:
episode_durations.append(t + 1)
episode_rewards.append(env.episode_total_reward)
if is_plot:
plot_durations(episode_durations, mean_durations)
plot_rewards(episode_rewards, mean_rewards)
break
print('Complete')
env.render(close=True)
env.close()
if is_plot:
plt.ioff()
plt.show()
## Store Results
np.save(file_name + '-dqn-rewards', episode_rewards)
np.save(file_name + '-dqn-durations', episode_durations)
return model, episode_rewards, episode_durations
import matplotlib.pyplot as plt
from network import Network, DQN
from memory import ReplayMemory
#%% hyper parameters
EPS_START = 0.9 # e-greedy threshold start value
EPS_END = 0.05 # e-greedy threshold end value
EPS_DECAY = 200 # e-greedy threshold decay
GAMMA = 0.8 # Q-learning discount factor
LR = 0.001 # NN optimizer learning rate
HIDDEN_LAYER = 256 # NN hidden layer size
BATCH_SIZE = 64 # Q-learning batch size
#%% DQN NETWORK ARCHITECTURE
model = DQN(4, 4, 4)
model.cuda()
optimizer = optim.Adam(model.parameters(), LR)
#%% SELECT ACTION USING GREEDY ALGORITHM
steps_done = 0
def select_action(state):
global steps_done
sample = random.random()
eps_threshold = EPS_END + (EPS_START - EPS_END) * math.exp(
-1. * steps_done / EPS_DECAY)
steps_done += 1
#print(state.shape)
#print(eps_threshold)
