print(env.action_space)
print(env.observation_space)
print(env.observation_space.high)
print(env.observation_space.low)
RL = DeepQNetwork(n_actions=3, n_features=2, learning_rate=0.001, gamma=0.9)
total_steps = 0
for i_episode in range(10):
observation = env.reset()
ep_r = 0
while True:
env.render()
action = RL.choose_action(observation)
observation_, reward, done, info = env.step(action)
position, velocity = observation_
# the higher the better
# reward = abs(position - (-0.3)) # r in [0, 1]
RL.save_experience(observation, action, reward, observation_)
if total_steps > 1000:
RL.learn()
ep_r += reward
def runner(node_num):
# Load checkpoint
load_path = "weights/weights.ckpt"
save_path = "weights/weights.ckpt"
# set seed
seed = 42
np.random.seed(seed)
random.seed(seed)
# Generate graph for training...
resources = 1
# G, reward_save, num_nodes = generate_graph(nodes=node_num, type='gnp_adversarial')
# G, reward_save, num_nodes = generate_graph(load_dir='../gml/ibm.gml', type='gml')
G, reward_save, num_nodes = generate_graph(nodes=node_num,
type='random_graph',
seed=42)
# Pick an arbitrary node to be the root
root = 0
# Try plotting. If on ssh, don't bother since there are some necessary plt.draw() commands
# to plot a networkx graph.
try:
plot_graph(G, root, 'rl_graph.png')
except:
print('No display')
# We may want to include the graph laplacian in the observation space
# Graph laplacian is D - A
# laplacian_matrix = nx.laplacian_matrix(G).toarray()
# flat_laplacian = laplacian_matrix.flatten()
# Build the learning environment
env = environment(G, [root], resources)
print('num_edges:', G.number_of_edges())
print("Ratio Heuristic", ratio_heuristic(G, [root], resources), '\n')
# Our observation space
n_y = len(env.actions_permutations)
# Initialize DQN
DQN = DeepQNetwork(
n_y=n_y,
n_x=num_nodes,
resources=resources,
env=env,
learning_rate=0.01,
replace_target_iter=20,
memory_size=20000,
batch_size=256,
reward_decay=0.6,
epsilon_min=0.1,
epsilon_greedy_decrement=5e-5,
# load_path=load_path,
# save_path=save_path,
# laplacian=flat_laplacian,
inner_act_func='leaky_relu',
output_act_func='leaky_relu')
episodes = 600
rewards = []
total_steps_counter = 0
episodes_since_max = 0
optimal_action_sequences = []
overall_start = time.time()
# DQN.epsilon = 0.5
for episode in range(episodes):
observation, done = env.reset()
episode_reward = 0
action_sequence = []
start = time.time()
train_time = 0
while not done:
# 1. Choose an action based on observation
action = DQN.choose_action(observation)
# check for random action
if action == -1:
# action = env.random_action()
# now choose between truly random action and a ratio action
r = random.random()
if r < 0.6:
action = env.random_action()
else:
action = env.ratio_action()
# save the taken action
action_sequence.append(action)
# print('Chosen action', action)
# 2. Take the chosen action in the environment
observation_, reward, done = env.step(action, neg=False)
# print(observation_, reward, done)
# 3. Store transition
DQN.store_transition(observation, action, reward, observation_)
episode_reward += reward
if total_steps_counter > 2000:
# 4. Train
s = time.time()
DQN.learn()
e = time.time()
train_time += (e - s)
if done:
rewards.append(episode_reward)
max_reward_so_far = np.amax(rewards)
# if maximum reward so far, save the action sequence
if episode_reward == max_reward_so_far:
optimal_action_sequences.append(
(action_sequence, episode_reward))
episodes_since_max = 0
# DQN.epsilon = 1
print("==========================================")
print("Episode: ", episode)
print("Reward: ", round(episode_reward, 2))
print("Epsilon: ", round(DQN.epsilon, 2))
print("Max reward so far: ", max_reward_so_far)
end = time.time()
print('Episode time:', end - start)
start = time.time()
break
# Save observation
observation = observation_
# Increase total steps
total_steps_counter += 1
# if episode == 700:
# DQN.epsilon_min = .1
# DQN.epsilon = 0.5
episodes_since_max += 1
print('train time across episode', train_time)
overall_end = time.time()
# TEST Q-Learning
DQN.epsilon = 0
DQN.epsilon_min = 0
observation, done = env.reset()
final_reward = 0
action_sequence = []
while not done:
action = DQN.choose_action(observation)
action_sequence.append(action)
observation_, reward, done = env.step(action, neg=False)
final_reward += reward
if done:
rewards.append(final_reward)
max_reward_so_far = np.amax(rewards)
# if maximum reward so far, save the action sequence
if final_reward == max_reward_so_far:
optimal_action_sequences.append(
(action_sequence, final_reward))
episodes_since_max = 0
break
# Save observation
observation = observation_
print('final epsilon=0 reward', final_reward, '\n')
# TESTING
# convert our 'best' optimal action sequence to the vector representation, test it for correctness
opt = optimal_action_sequences[len(optimal_action_sequences) - 1][0]
reward = optimal_action_sequences[len(optimal_action_sequences) - 1][1]
print()
# print('RL action sequence:')
env.reset()
true_r = 0
for action in opt:
# print('action index', action)
# debug will print the action at each step as a vector
_, r, d = env.step(action, debug=True)
true_r += r
results = []
# if we have a reasonable number of nodes (< 24), we can compute optimal using DP
if num_nodes < 24:
dp_time = time.time()
results.append(DP_optimal(G, [root], resources))
print('DP Opt: ', results[0])
dp_time_end = time.time()
results.append(dp_time_end - dp_time)
print('DP time: ', results[1])
else:
results.append('n/a')
results.append('n/a')
print('\n Random Heuristic', random_heuristic(G, [root], resources), '\n')
results.append(random_heuristic(G, [root], resources))
# Only works on trees
# print('\n Tree Heuristic:', simulate_tree_recovery(G, resources, root, clean=False), '\n')
ratio_time_start = time.time()
print('\n Ratio Heuristic', ratio_heuristic(G, [root], resources))
ratio_time_end = time.time()
print('Ratio time:', ratio_time_end - ratio_time_start)
results.append(ratio_heuristic(G, [root], resources))
results.append(ratio_time_end - ratio_time_start)
print('\n reward during training:', reward)
results.append(reward)
print('RL method time (s): ', overall_end - overall_start, '\n')
results.append(overall_end - overall_start)
plot_bar_x(rewards, 'episode', 'reward_graph.png')
with open(reward_save, 'w') as f:
for item in rewards:
f.write('%s\n' % item)
return results
RENDER_ENV = False
EPISODES = 500
rewards = []
RENDER_REWARD_MIN = 0
total_steps_counter = 0
for episode in range(400):
observation = env.reset()
episode_reward = 0
while True:
if RENDER_ENV: env.render()
# 1. Choose an action based on observation
action = DQN.choose_action(observation)
# 2. Take the chosen action in the environment
observation_, reward, done, info = env.step(action)
# 3. Store transition
DQN.store_transition(observation, action, reward, observation_)
episode_reward += reward
if total_steps_counter > 1000:
# 4. Train
DQN.learn()
if done:
rewards.append(episode_reward)