class DDQN:
""" Deep Q-Learning Main Algorithm
"""
def __init__(self, action_dim, state_dim, args):
""" Initialization
"""
# Environment and DDQN parameters
self.with_per = args.with_per
self.action_dim = action_dim
self.state_dim = (args.consecutive_frames,) + state_dim
#
self.lr = 2.5e-4
self.gamma = 0.95
self.epsilon = 0.8
self.epsilon_decay = 0.99
self.buffer_size = 20000
#
if(len(state_dim) < 3):
self.tau = 1e-2
else:
self.tau = 1.0
# Create actor and critic networks
self.agent = Agent(self.state_dim, action_dim, self.lr, self.tau, args.dueling)
# Memory Buffer for Experience Replay
self.buffer = MemoryBuffer(self.buffer_size, args.with_per)
def policy_action(self, s):
""" Apply an espilon-greedy policy to pick next action
"""
if random() <= self.epsilon:
return randrange(self.action_dim)
else:
return np.argmax(self.agent.predict(s)[0])
def train_agent(self, batch_size):
""" Train Q-network on batch sampled from the buffer
"""
# Sample experience from memory buffer (optionally with PER)
s, a, r, d, new_s, idx = self.buffer.sample_batch(batch_size)
# Apply Bellman Equation on batch samples to train our DDQN
q = self.agent.predict(s)
next_q = self.agent.predict(new_s)
q_targ = self.agent.target_predict(new_s)
for i in range(s.shape[0]):
old_q = q[i, a[i]]
if d[i]:
q[i, a[i]] = r[i]
else:
next_best_action = np.argmax(next_q[0,:])
q[i, a[i]] = r[i] + self.gamma * q_targ[i, next_best_action]
if(self.with_per):
# Update PER Sum Tree
self.buffer.update(idx[i], abs(old_q - q[i, a[i]]))
# Train on batch
self.agent.fit(s, q)
# Decay epsilon
self.epsilon *= self.epsilon_decay
def train(self, env, args, summary_writer):
""" Main DDQN Training Algorithm
"""
results = []
tqdm_e = tqdm(range(args.nb_episodes), desc='Score', leave=True, unit=" episodes")
for e in tqdm_e:
# Reset episode
time, cumul_reward, done = 0, 0, False
old_state = env.reset()
while not done:
if args.render: env.render()
# Actor picks an action (following the policy)
a = self.policy_action(old_state)
# Retrieve new state, reward, and whether the state is terminal
new_state, r, done, _ = env.step(a)
# Memorize for experience replay
self.memorize(old_state, a, r, done, new_state)
# Update current state
old_state = new_state
cumul_reward += r
time += 1
# Train DDQN and transfer weights to target network
if(self.buffer.size() > args.batch_size):
self.train_agent(args.batch_size)
self.agent.transfer_weights()
# Gather stats every episode for plotting
if(args.gather_stats):
mean, stdev = gather_stats(self, env)
results.append([e, mean, stdev])
# Export results for Tensorboard
score = tfSummary('score', cumul_reward)
summary_writer.add_summary(score, global_step=e)
summary_writer.flush()
# Display score
tqdm_e.set_description("Score: " + str(cumul_reward))
tqdm_e.refresh()
return results
def memorize(self, state, action, reward, done, new_state):
""" Store experience in memory buffer
"""
if(self.with_per):
q_val = self.agent.predict(new_state)
q_val_t = self.agent.target_predict(new_state)
next_best_action = np.argmax(q_val)
new_val = reward + self.gamma * q_val_t[0, next_best_action]
td_error = abs(new_val - q_val)[0]
else:
td_error = 0
self.buffer.memorize(state, action, reward, done, new_state, td_error)
class DDQN:
""" Deep Q-Learning Main Algorithm 深度Q学习主要算法
"""
def __init__(self, action_dim, state_dim, args):
""" Initialization 初始化
"""
# Environment and DDQN parameters 环境和DDQN参数
self.with_per = args.with_per
self.action_dim = action_dim
self.state_dim = (args.consecutive_frames, ) + state_dim
#
self.lr = 2.5e-4
self.gamma = 0.95
self.epsilon = 0.8
self.epsilon_decay = 0.99
self.buffer_size = 20000
#
if (len(state_dim) < 3):
self.tau = 1e-2
else:
self.tau = 1.0
# Create actor and critic networks 建立演员和评论家网络
self.agent = Agent(self.state_dim, action_dim, self.lr, self.tau,
args.dueling)
# Memory Buffer for Experience Replay 用于经验重播的内存缓冲区
self.buffer = MemoryBuffer(self.buffer_size, args.with_per)
def policy_action(self, s):
""" Apply an espilon-greedy policy to pick next action 应用epsilon-greedy策略选择下一步操作
"""
if random() <= self.epsilon:
return randrange(self.action_dim)
else:
return np.argmax(self.agent.predict(s)[0])
def train_agent(self, batch_size):
""" Train Q-network on batch sampled from the buffer 从缓冲区采样的批次训练Q网络
"""
# Sample experience from memory buffer (optionally with PER) 来自内存缓冲区的示例体验（可选配PER）
s, a, r, d, new_s, idx = self.buffer.sample_batch(batch_size)
# Apply Bellman Equation on batch samples to train our DDQN 在批次样本里应用Bellman方程来训练我们的DDQN
q = self.agent.predict(s)
next_q = self.agent.predict(new_s)
q_targ = self.agent.target_predict(new_s)
for i in range(s.shape[0]):
old_q = q[i, a[i]]
if d[i]:
q[i, a[i]] = r[i]
else:
next_best_action = np.argmax(next_q[i, :])
q[i, a[i]] = r[i] + self.gamma * q_targ[i, next_best_action]
if (self.with_per):
# Update PER Sum Tree 更新PER Sum树
self.buffer.update(idx[i], abs(old_q - q[i, a[i]]))
# Train on batch 批量训练
self.agent.fit(s, q)
# Decay epsilon 衰变epsilon
self.epsilon *= self.epsilon_decay
def train(self, env, args, summary_writer):
""" Main DDQN Training Algorithm DDQN主要训练算法
"""
results = []
tqdm_e = tqdm(range(args.nb_episodes),
desc='Score',
leave=True,
unit=" episodes")
for e in tqdm_e:
# Reset episode 重设episode
time, cumul_reward, done = 0, 0, False
old_state = env.reset()
while not done:
if args.render: env.render()
# Actor picks an action (following the policy) 演员选择动作（遵循政策）
a = self.policy_action(old_state)
# Retrieve new state, reward, and whether the state is terminal 检索新状态，奖励以及该状态是否为终端
new_state, r, done, _ = env.step(a)
# Memorize for experience replay 保存经验重播
self.memorize(old_state, a, r, done, new_state)
# Update current state 更新当前状态
old_state = new_state
cumul_reward += r
time += 1
# Train DDQN and transfer weights to target network 训练DDQN并将权重转移到目标网络
if (self.buffer.size() > args.batch_size):
self.train_agent(args.batch_size)
self.agent.transfer_weights()
# Gather stats every episode for plotting 收集每个情节的统计数据以进行绘图
if (args.gather_stats):
mean, stdev = gather_stats(self, env)
results.append([e, mean, stdev])
# Export results for Tensorboard 为Tensorboard导出结果
score = tfSummary('score', cumul_reward)
summary_writer.add_summary(score, global_step=e)
summary_writer.flush()
# Display score 显示分数
tqdm_e.set_description("Score: " + str(cumul_reward))
tqdm_e.refresh()
return results
def memorize(self, state, action, reward, done, new_state):
""" Store experience in memory buffer 将经验存储在内存缓冲区中
"""
if (self.with_per):
q_val = self.agent.predict(state)
q_val_t = self.agent.target_predict(new_state)
next_best_action = np.argmax(self.agent.predict(new_state))
new_val = reward + self.gamma * q_val_t[0, next_best_action]
td_error = abs(new_val - q_val)[0]
else:
td_error = 0
self.buffer.memorize(state, action, reward, done, new_state, td_error)
def save_weights(self, path):
path += '_LR_{}'.format(self.lr)
if (self.with_per):
path += '_PER'
self.agent.save(path)
def load_weights(self, path):
self.agent.load_weights(path)
class DDPG:
""" Deep Deterministic Policy Gradient (DDPG) Helper Class
"""
def __init__(self, act_dim, env_dim, act_range, k, buffer_size = 20000, gamma = 0.99, lr = 0.00005, tau = 0.001):
""" Initialization
"""
# Environment and A2C parameters
self.act_dim = act_dim
self.act_range = act_range
self.env_dim = (k,) + env_dim
self.gamma = gamma
self.lr = lr
# Create actor and critic networks
self.actor = Actor(self.env_dim, act_dim, act_range, 0.1 * lr, tau)
self.critic = Critic(self.env_dim, act_dim, lr, tau)
self.buffer = MemoryBuffer(buffer_size)
def policy_action(self, s):
""" Use the actor to predict value
"""
return self.actor.predict(s)[0]
def bellman(self, rewards, q_values, dones):
""" Use the Bellman Equation to compute the critic target
"""
critic_target = np.asarray(q_values)
for i in range(q_values.shape[0]):
if dones[i]:
critic_target[i] = rewards[i]
else:
critic_target[i] = rewards[i] + self.gamma * q_values[i]
return critic_target
def memorize(self, state, action, reward, done, new_state):
""" Store experience in memory buffer
"""
self.buffer.memorize(state, action, reward, done, new_state)
def sample_batch(self, batch_size):
return self.buffer.sample_batch(batch_size)
def update_models(self, states, actions, critic_target):
""" Update actor and critic networks from sampled experience
"""
# Train critic
self.critic.train_on_batch(states, actions, critic_target)
# Q-Value Gradients under Current Policy
actions = self.actor.model.predict(states)
grads = self.critic.gradients(states, actions)
# Train actor
self.actor.train(states, actions, np.array(grads).reshape((-1, self.act_dim)))
# Transfer weights to target networks at rate Tau
self.actor.transfer_weights()
self.critic.transfer_weights()
def train(self, env, args, summary_writer):
results = []
# First, gather experience
tqdm_e = tqdm(range(args.nb_episodes), desc='Score', leave=True, unit=" episodes")
for e in tqdm_e:
# Reset episode
time, cumul_reward, done = 0, 0, False
old_state = env.reset()
actions, states, rewards = [], [], []
noise = OrnsteinUhlenbeckProcess(size=self.act_dim)
while not done:
if args.render: env.render()
# Actor picks an action (following the deterministic policy)
a = self.policy_action(old_state)
# Clip continuous values to be valid w.r.t. environment
a = np.clip(a+noise.generate(time), -self.act_range, self.act_range)
# Retrieve new state, reward, and whether the state is terminal
new_state, r, done, _ = env.step(a)
# Add outputs to memory buffer
self.memorize(old_state, a, r, done, new_state)
# Sample experience from buffer
states, actions, rewards, dones, new_states, _ = self.sample_batch(args.batch_size)
# Predict target q-values using target networks
q_values = self.critic.target_predict([new_states, self.actor.target_predict(new_states)])
# Compute critic target
critic_target = self.bellman(rewards, q_values, dones)
# Train both networks on sampled batch, update target networks
self.update_models(states, actions, critic_target)
# Update current state
old_state = new_state
cumul_reward += r
time += 1
# Gather stats every episode for plotting
if(args.gather_stats):
mean, stdev = gather_stats(self, env)
results.append([e, mean, stdev])
# Export results for Tensorboard
score = tfSummary('score', cumul_reward)
summary_writer.add_summary(score, global_step=e)
summary_writer.flush()
# Display score
tqdm_e.set_description("Score: " + str(cumul_reward))
tqdm_e.refresh()
return results
def save_weights(self, path):
path += '_LR_{}'.format(self.lr)
self.actor.save(path)
self.critic.save(path)
def load_weights(self, path_actor, path_critic):
self.critic.load_weights(path_critic)
self.actor.load_weights(path_actor)
class DDQN:
""" Deep Q-Learning Main Algorithm
"""
def __init__(self, action_dim, state_dim, args):
""" Initialization
"""
# Environment and DDQN parameters
self.with_per = args.with_per
self.action_dim = action_dim
self.state_dim = state_dim
#
self.lr = 2.5e-4
self.gamma = 0.95
self.epsilon = 0.8
self.epsilon_decay = 0.99
self.buffer_size = 20000
#
self.tau = 1e-2
# Create actor and critic networks
self.agent = Agent(self.state_dim, action_dim, self.lr, self.tau,
args.dueling, args.hidden_dim)
# Memory Buffer for Experience Replay
self.buffer = MemoryBuffer(self.buffer_size, args.with_per)
def policy_action(self, s):
""" Apply an espilon-greedy policy to pick next action
"""
if random() <= self.epsilon:
return randrange(self.action_dim)
else:
return np.argmax(self.agent.predict(s)[0])
def train_agent(self, batch_size):
""" Train Q-network on batch sampled from the buffer
"""
# Sample experience from memory buffer (optionally with PER)
s, a, r, d, new_s, idx = self.buffer.sample_batch(batch_size)
# Apply Bellman Equation on batch samples to train our DDQN
q = self.agent.predict(s)
next_q = self.agent.predict(new_s)
q_targ = self.agent.target_predict(new_s)
for i in range(s.shape[0]):
old_q = q[i, a[i]]
if d[i]:
q[i, a[i]] = r[i]
else:
next_best_action = np.argmax(next_q[i, :])
q[i, a[i]] = r[i] + self.gamma * q_targ[i, next_best_action]
if (self.with_per):
# Update PER Sum Tree
self.buffer.update(idx[i], abs(old_q - q[i, a[i]]))
# Train on batch
self.agent.fit(s, q)
# Decay epsilon
self.epsilon *= self.epsilon_decay
def train(self, env, args, summary_writer, envtest=None):
""" Main DDQN Training Algorithm
"""
results = []
tqdm_e = tqdm(range(args.nb_episodes),
desc='Score',
leave=True,
unit=" episodes")
epoch = 0
gross_profit = 0
WritetoCsvFile("logFile_1.csv", [
"stage", "file", "history_win", "stop", "usevol", "dueling",
"traineval", "allprices", "allprices2", "allprices3", "ma1", "ma2",
"madifference", "hidema", "candlenum", "hidden_dim", "maxProfit",
"maxLOSS", "avgProfit", "avgLOSS", "countprofit", "countloss",
"maxdrop", "Total profit", "total_reward", "TRADES", "epoch"
])
WritetoCsvFile("logFileDetail.csv", [
"stage", "file", "history_win", "stop", "usevol", "dueling",
"traineval", "allprices", "allprices2", "allprices3", "ma1", "ma2",
"madifference", "hidema", "candlenum", "hidden_dim", 'maxProfit',
'maxLOSS', 'avgProfit', 'avgLOSS', 'maxdrop', 'Total profit',
'gross profit', "total_reward", 'TRADES', 'epoch'
])
for e in tqdm_e:
# Reset episode
time, cumul_reward, done = 0, 0, False
old_state = env.reset()
##########################################
total_reward = 0
total_profit = 0
total_loss = 0
total_profitMax = 0
total_profitMin = 0
max_drop = 0
profitLst = []
lossLst = []
trades = 0
step = 0
#####################################3####
while not done:
#if args.render: env.render()
# Actor picks an action (following the policy)
a = self.policy_action(old_state)
# Retrieve new state, reward, and whether the state is terminal
#new_state, r, done, _ = env.step(a)
#######################################################
new_state, r, done, buy, sell, profit = env.step(a)
total_reward += r
if profit != 0:
trades += 1
total_profit += profit
if total_profit > total_profitMax:
total_profitMax = total_profit
total_profitMin = total_profit
if total_profit < total_profitMin:
total_profitMin = total_profit
try:
if total_profitMax != 0 and max_drop < (
total_profitMax -
total_profitMin) / total_profitMax:
max_drop = (total_profitMax -
total_profitMin) / total_profitMax
except:
max_drop = 0
if profit > 0:
profitLst.append(profit)
elif profit < 0:
lossLst.append(profit)
step += 1
if step % 1500 == 0:
print(
'maxProfit: {} maxLOSS: {} avgProfit: {:01.2f} avgLOSS: {:01.2f} maxdrop: {:.2%} Total profit: {}/{} TRADES: {} '
.format(np.max(profitLst + [0]),
-np.min(lossLst + [0]),
np.mean(profitLst + [0]),
-np.mean(lossLst + [0]), max_drop,
total_profit, gross_profit, trades))
WritetoCsvFile("logFileDetail.csv", [
"train", args.trainf, args.history_win, args.stop,
args.usevol, args.dueling, args.traineval,
args.allprices, args.allprices2, args.allprices3,
args.ma1, args.ma2, args.madifference, args.hidema,
args.candlenum, args.hidden_dim,
np.max(profitLst + [0]), -np.min(lossLst + [0]),
np.mean(profitLst + [0]), -np.mean(lossLst + [0]),
max_drop, total_profit, gross_profit, total_reward,
trades, epoch
])
#done = True if step == len(env.data) - 3 else False
######################################################
# Memorize for experience replay
self.memorize(old_state, a, r, done, new_state)
# Update current state
old_state = new_state
cumul_reward += r
time += 1
# Train DDQN and transfer weights to target network
if (self.buffer.size() > args.batch_size):
self.train_agent(args.batch_size)
self.agent.transfer_weights()
gross_profit += total_profit
# Gather stats every episode for plotting
if (args.gather_stats):
mean, stdev = gather_stats(self, env)
results.append([e, mean, stdev])
# Export results for Tensorboard
score = tfSummary('score', cumul_reward)
l_profit = tfSummary('profit', total_profit)
l_aprofit = tfSummary('average profit', np.mean(profitLst))
l_aloss = tfSummary('l_aloss', -np.mean(lossLst))
l_trades = tfSummary('l_trades', trades)
np.mean(profitLst), -np.mean(lossLst)
summary_writer.add_summary(score, global_step=e)
summary_writer.add_summary(l_profit, global_step=e)
summary_writer.add_summary(l_aprofit, global_step=e)
summary_writer.add_summary(l_aloss, global_step=e)
summary_writer.add_summary(l_trades, global_step=e)
summary_writer.flush()
# Display score
tqdm_e.set_description("Score: " + str(cumul_reward))
tqdm_e.refresh()
self.agent.saveModel("./models/model_ep", "")
results = [
np.max(profitLst + [0]), -np.min(lossLst + [0]),
np.mean(profitLst + [0]), -np.mean(lossLst + [0]),
len(profitLst),
len(lossLst), max_drop, total_profit, total_reward, trades
]
WritetoCsvFile("logFile_1.csv", [
"train", args.trainf, args.history_win, args.stop, args.usevol,
args.dueling, args.traineval, args.allprices, args.allprices2,
args.allprices3, args.ma1, args.ma2, args.madifference,
args.hidema, args.candlenum, args.hidden_dim
] + results + [epoch])
if envtest: # Если задано окружение для тестирования то тестируем каждую эпоху
newargs = args
newargs.traineval = False
self.evaluate(envtest,
newargs,
summary_writer,
model=None,
epoch=epoch)
epoch += 1
return results
def memorize(self, state, action, reward, done, new_state):
""" Store experience in memory buffer
"""
if (self.with_per):
q_val = self.agent.predict(state)
q_val_t = self.agent.target_predict(new_state)
next_best_action = np.argmax(q_val)
new_val = reward + self.gamma * q_val_t[0, next_best_action]
td_error = abs(new_val - q_val)[0]
else:
td_error = 0
self.buffer.memorize(state, action, reward, done, new_state, td_error)
def evaluate(self, env, args, summary_writer, model, epoch=0):
""" Evaluate """
results = []
if model:
self.agent.loadModel_versoin(model, "")
done = False
old_state = env.reset()
##########################################
total_reward = 0
total_profit = 0
total_loss = 0
total_profitMax = 0
total_profitMin = 0
max_drop = 0
profitLst = []
lossLst = []
step = 0
trades = 0
#####################################3####
while not done:
# if args.render: env.render()
# Actor picks an action (following the policy)
a = self.policy_action(old_state)
# Retrieve new state, reward, and whether the state is terminal
new_state, r, done, buy, sell, profit = env.step(a)
#######################################################
total_reward += r
if profit != 0:
trades += 1
total_profit += profit
if total_profit > total_profitMax:
total_profitMax = total_profit
total_profitMin = total_profit
if total_profit < total_profitMin:
total_profitMin = total_profit
try:
if total_profitMax != 0 and max_drop < (
total_profitMax -
total_profitMin) / total_profitMax:
max_drop = (total_profitMax -
total_profitMin) / total_profitMax
except:
max_drop = 0
if profit > 0:
profitLst.append(profit)
elif profit < 0:
lossLst.append(profit)
step += 1
if step % 1500 == 0:
print(
'maxProfit: {} maxLOSS: {} avgProfit: {:01.2f} avgLOSS: {:01.2f} maxdrop: {:.2%} Total profit: {} Total reward: {} TRADES: {} '
.format(np.max(profitLst + [0]), -np.min(lossLst + [0]),
np.mean(profitLst + [0]), -np.mean(lossLst + [0]),
max_drop, total_profit, total_reward, trades))
WritetoCsvFile("logFileDetail.csv", [
"eval", args.trainf, args.history_win, args.stop,
args.usevol, args.dueling, args.traineval, args.allprices,
args.allprices2, args.allprices3, args.ma1, args.ma2,
args.madifference, args.hidema, args.candlenum,
args.hidden_dim,
np.max(profitLst + [0]), -np.min(lossLst + [0]),
np.mean(profitLst + [0]), -np.mean(lossLst + [0]),
max_drop, total_profit, total_profit, total_reward, trades,
epoch
])
#done = True if step == len(env.data) - 2 else False
######################################################
# Memorize for experience replay
if args.traineval:
self.memorize(old_state, a, r, done, new_state)
# Train DDQN and transfer weights to target network
if (self.buffer.size() > args.batch_size):
self.train_agent(args.batch_size)
self.agent.transfer_weights()
# Update current state
old_state = new_state
print(
'maxProfit: {} maxLOSS: {} avgProfit: {:01.2f} avgLOSS: {:01.2f} maxdrop: {:.2%} Total profit: {} Total reward: {} TRADES: {} '
.format(np.max(profitLst + [0]), -np.min(lossLst + [0]),
np.mean(profitLst + [0]), -np.mean(lossLst + [0]),
max_drop, total_profit, total_reward, trades))
results = [
np.max(profitLst + [0]), -np.min(lossLst + [0]),
np.mean(profitLst + [0]), -np.mean(lossLst + [0]),
len(profitLst),
len(lossLst), max_drop, total_profit, total_reward, trades
]
WritetoCsvFile("logFile_1.csv", [
"eval", args.trainf, args.history_win, args.stop, args.usevol,
args.dueling, args.traineval, args.allprices, args.allprices2,
args.allprices3, args.ma1, args.ma2, args.madifference,
args.hidema, args.candlenum, args.hidden_dim
] + results + [epoch])
return results
class ddpgAgent():
"""Deep Deterministic Policy Gradient(DDPG) Agent
"""
def __init__(self, env_, is_discrete=False, batch_size=100, w_per=True):
# gym environments
self.env = env_
self.discrete = is_discrete
self.obs_dim = env_.observation_space.shape[0]
self.act_dim = env_.action_space.n if is_discrete else env_.action_space.shape[
0]
self.action_bound = (env_.action_space.high - env_.action_space.low
) / 2 if not is_discrete else 1.
self.action_shift = (env_.action_space.high + env_.action_space.low
) / 2 if not is_discrete else 0.
# initialize actor & critic and its targets
self.discount_factor = 0.99
self.actor = ActorNet(self.obs_dim,
self.act_dim,
self.action_bound,
lr_=1e-4,
tau_=1e-3)
self.critic = CriticNet(self.obs_dim,
self.act_dim,
lr_=1e-3,
tau_=1e-3,
discount_factor=self.discount_factor)
# Experience Buffer
self.buffer = MemoryBuffer(BUFFER_SIZE, with_per=w_per)
self.with_per = w_per
self.batch_size = batch_size
# OU-Noise-Process
self.noise = OrnsteinUhlenbeckProcess(size=self.act_dim)
###################################################
# Network Related
###################################################
def make_action(self, obs, t, noise=True):
""" predict next action from Actor's Policy
"""
action_ = self.actor.predict(obs)[0]
a = np.clip(action_ + self.noise.generate(t) if noise else 0,
-self.action_bound, self.action_bound)
return a
def update_networks(self, obs, acts, critic_target):
""" Train actor & critic from sampled experience
"""
# update critic
self.critic.train(obs, acts, critic_target)
# get next action and Q-value Gradient
n_actions = self.actor.network.predict(obs)
q_grads = self.critic.Qgradient(obs, n_actions)
# update actor
self.actor.train(obs, self.critic.network, q_grads)
# update target networks
self.actor.target_update()
self.critic.target_update()
def replay(self, replay_num_):
if self.with_per and (self.buffer.size() <= self.batch_size): return
for _ in range(replay_num_):
# sample from buffer
states, actions, rewards, dones, new_states, idx = self.sample_batch(
self.batch_size)
# get target q-value using target network
q_vals = self.critic.target_predict(
[new_states, self.actor.target_predict(new_states)])
# bellman iteration for target critic value
critic_target = np.asarray(q_vals)
for i in range(q_vals.shape[0]):
if dones[i]:
critic_target[i] = rewards[i]
else:
critic_target[
i] = self.discount_factor * q_vals[i] + rewards[i]
if self.with_per:
self.buffer.update(idx[i],
abs(q_vals[i] - critic_target[i]))
# train(or update) the actor & critic and target networks
self.update_networks(states, actions, critic_target)
####################################################
# Buffer Related
####################################################
def memorize(self, obs, act, reward, done, new_obs):
"""store experience in the buffer
"""
if self.with_per:
q_val = self.critic.network(
[np.expand_dims(obs, axis=0),
self.actor.predict(obs)])
next_action = self.actor.target_network.predict(
np.expand_dims(new_obs, axis=0))
q_val_t = self.critic.target_predict(
[np.expand_dims(new_obs, axis=0), next_action])
new_val = reward + self.discount_factor * q_val_t
td_error = abs(new_val - q_val)[0]
else:
td_error = 0
self.buffer.memorize(obs, act, reward, done, new_obs, td_error)
def sample_batch(self, batch_size):
""" Sampling from the batch
"""
return self.buffer.sample_batch(batch_size)
###################################################
# Save & Load Networks
###################################################
def save_weights(self, path):
""" Agent's Weights Saver
"""
self.actor.save_network(path)
self.critic.save_network(path)
def load_weights(self, pretrained):
""" Agent's Weights Loader
"""
self.actor.load_network(pretrained)
self.critic.load_network(pretrained)
class DDPG:
""" Deep Deterministic Policy Gradient (DDPG) Helper Class
"""
def __init__(self,
act_dim,
env_dim,
act_range,
k,
buffer_size=20000,
gamma=0.99,
lr=0.00005,
tau=0.001):
""" Initialization
"""
# Environment and A2C parameters
self.act_dim = act_dim
self.act_range = act_range
self.env_dim = (40, )
self.gamma = gamma
self.lr = lr
# Create actor and critic networks
self.actor = Actor(self.env_dim, act_dim, act_range, 0.1 * lr, tau)
self.critic = Critic(self.env_dim, act_dim, lr, tau)
self.buffer = MemoryBuffer(buffer_size)
def policy_action(self, s):
""" Use the actor to predict value
"""
return self.actor.predict(s)[0]
def bellman(self, rewards, q_values, dones):
""" Use the Bellman Equation to compute the critic target
"""
critic_target = np.asarray(q_values)
for i in range(q_values.shape[0]):
if dones[i]:
critic_target[i] = rewards[i]
else:
critic_target[i] = rewards[i] + self.gamma * q_values[i]
return critic_target
def memorize(self, state, action, reward, done, new_state):
""" Store experience in memory buffer
"""
self.buffer.memorize(state, action, reward, done, new_state)
def sample_batch(self, batch_size):
return self.buffer.sample_batch(batch_size)
def update_models(self, states, actions, critic_target):
""" Update actor and critic networks from sampled experience
"""
# Train critic
self.critic.train_on_batch(states, actions, critic_target)
# Q-Value Gradients under Current Policy
actions = self.actor.model.predict(states)
grads = self.critic.gradients(states, actions)
# Train actor
self.actor.train(states, actions,
np.array(grads).reshape((-1, self.act_dim)))
# Transfer weights to target networks at rate Tau
self.actor.transfer_weights()
self.critic.transfer_weights()
def train(self, summary_writer):
env = CarEnv()
results = []
i = 0
# First, gather experience
tqdm_e = tqdm(range(2000), desc='Score', leave=True, unit=" episodes")
for e in tqdm_e:
# Reset episode
time, cumul_reward, done = 0, 0, False
old_state = env.reset()
old_state = np.array(old_state).reshape(40, )
actions, states, rewards = [], [], []
noise = OrnsteinUhlenbeckProcess(size=self.act_dim)
while not done:
# if args.render: env.render()
# Actor picks an action (following the deterministic policy)
a = self.policy_action(old_state)
# Clip continuous values to be valid w.r.t. environment
a = np.clip(a + noise.generate(time), -self.act_range,
self.act_range)
a = float(a[0])
# Retrieve new state, reward, and whether the state is terminal
new_state, r, done, _ = env.step(a, time)
print("Now r is {}".format(r))
# Add outputs to memory buffer
temp_next = old_state.copy()
temp_next[:4] = temp_next[4:8]
temp_next[4:8] = temp_next[8:12]
temp_next[8:12] = temp_next[12:16]
temp_next[12:16] = temp_next[16:20]
temp_next[16:20] = temp_next[20:24]
temp_next[20:24] = temp_next[24:28]
temp_next[24:28] = temp_next[28:32]
temp_next[28:32] = temp_next[32:36]
temp_next[32:36] = temp_next[36:40]
temp_next[36:40] = new_state
temp_next = np.array(temp_next).reshape(40, )
self.memorize(old_state, a, r, done, temp_next)
old_state = temp_next.copy()
cumul_reward += r
time += 1
# since episode is over destroying actors in the scenario
for actor in env.actor_list:
actor.destroy()
# Sample experience from buffer
for i in range(50):
states, actions, rewards, dones, new_states, _ = self.sample_batch(
64)
# Predict target q-values using target networks
q_values = self.critic.target_predict(
[new_states,
self.actor.target_predict(new_states)])
# Compute critic target
critic_target = self.bellman(rewards, q_values, dones)
# Train both networks on sampled batch, update target networks
self.update_models(states, actions, critic_target)
print("learning happened")
# mean, stdev, velocity_data, action_data, vehicle_x_data, vehicle_y_data, obj_x_data, obj_y_data = gather_stats(self, env, velocity_data, action_data, vehicle_x_data, vehicle_y_data, obj_x_data, obj_y_data)
mean, stdev = gather_stats(self, env)
results.append([e, mean, stdev])
# Export results for Tensorboard
print(cumul_reward)
score = tfSummary('score', cumul_reward)
summary_writer.add_summary(score, global_step=e)
summary_writer.flush()
# Display score
tqdm_e.set_description("Score: " + str(cumul_reward))
tqdm_e.refresh()
i += 1
if i % 10 == 0:
df = pd.DataFrame(np.array(results))
df.to_csv("DDPG" + "/logs.csv",
header=['Episode', 'Mean', 'Stddev'],
float_format='%10.5f')
return results
def save_weights(self, path):
path += '_LR_{}'.format(self.lr)
self.actor.save(path)
self.critic.save(path)
def load_weights(self, path_actor, path_critic):
self.critic.load_weights(path_critic)
self.actor.load_weights(path_actor)
class Agent:
""" Stock Trading Bot """
def __init__(self,
buffer_size,
state_size,
action_size=3,
learning_rate=0.001):
# agent config
self.buffer = MemoryBuffer(buffer_size, True)
self.state_size = state_size
self.action_size = action_size
self.inventory = []
# model config
self.gamma = 0.95 # affinity for long term reward
self.loss = huber_loss
self.optimizer = Adam(lr=learning_rate)
# target network
self.model = self._model()
self.target_model = clone_model(self.model)
self.target_model.set_weights(self.model.get_weights())
def _model(self):
inputs = Input(shape=self.state_size)
x = Dense(64, activation='relu')(inputs)
x = Dense(128, activation='relu')(x)
value = Dense(self.action_size, activation='linear')(x)
a = Dense(self.action_size, activation='linear')(x)
meam = Lambda(lambda x: K.mean(x, axis=1, keepdims=True))(a)
advantage = Subtract()([a, meam])
q = Add()([value, advantage])
model = Model(inputs=inputs, outputs=q)
model.compile(loss=self.loss, optimizer=self.optimizer)
return model
def remember(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
def act(self, state, epsilon, is_eval=False):
# take random action in order to diversify experience at the beginning
if not is_eval and random.random() <= epsilon:
return random.randrange(self.action_size)
state = state.reshape((-1, ) + self.state_size)
action_probs = self.model.predict(state)
return np.argmax(action_probs[0])
def epsilon_decay(self, epsilon, epsilon_min, epsilon_decay):
if epsilon > epsilon_min:
epsilon *= epsilon_decay
return epsilon
def remember_sumtree(
self,
state,
action,
reward,
new_state,
done,
):
state = state.reshape((-1, ) + self.state_size)
new_state = new_state.reshape((-1, ) + self.state_size)
q_val = self.model.predict(state)
q_val_t = self.target_model.predict(new_state)
next_best_action = np.argmax(q_val)
new_val = reward + self.gamma * q_val_t[0, next_best_action]
td_error = abs(new_val - q_val + 1e-8)[0]
self.buffer.memorize(state, action, reward, done, new_state, td_error)
def target_model_update(self,
done,
tau=0.1,
type='reset',
reset_every=5000):
if type == 'reset':
if self.n_iter % reset_every == 0:
print('update target model')
# reset target model weights
self.target_model.set_weights(self.model.get_weights())
if type == 'transfer':
if done:
W = self.model.get_weights()
tgt_W = self.target_model.get_weights()
for i in range(len(W)):
tgt_W[i] = tau * W[i] + (1 - tau) * tgt_W[i]
self.target_model.set_weights(tgt_W)
def train_experience_replay_sumtree(
self,
batch_size,
):
state, action, reward, done, new_state, idx = self.buffer.sample_batch(
batch_size)
state = state.reshape((-1, ) + self.state_size)
new_state = new_state.reshape((-1, ) + self.state_size)
q = self.model.predict(state)
next_q = self.model.predict(new_state)
q_targ = self.target_model.predict(new_state)
for i in range(state.shape[0]):
old_q = q[i, action[i]]
if done[i]:
q[i, action[i]] = reward[i]
else:
next_best_action = np.argmax(next_q[i, :])
q[i, action[i]] = reward[i] + self.gamma * q_targ[
i, next_best_action]
self.buffer.update(idx[i], abs(old_q - q[i, action[i]]))
loss = self.model.fit((state), q, epochs=1,
verbose=0).history["loss"][0]
return loss
def save(self, name):
if not os.path.exists('save/' + name):
os.makedirs('save/' + name)
np.save('save/' + name + '/data.npy', self.buffer.buffer.data)
np.save('save/' + name + '/tree.npy', self.buffer.buffer.tree)
self.model.save('save/' + name + '/model.h5')
self.target_model.save('save/' + name + '/target_model.h5')
else:
print('already exist, please check.')
def load(self, name):
if not os.path.exists('save/' + name):
print('not exist, please check.')
else:
self.buffer.buffer.data = np.load('save/' + name + '/data.npy',
allow_pickle=True)
self.buffer.buffer.tree = np.load('save/' + name + '/tree.npy',
allow_pickle=True)
self.model = load_model('save/' + name + '/model.h5')
self.target_model = load_model('save/' + name + '/target_model.h5')
class DDPG(object):
""" Deep Deterministic Policy Gradient (DDPG) Helper Class
"""
def __init__(self,
action_dim,
state_dim,
batch_size,
step,
buffer_size,
train_indicator,
episode,
gamma,
lra,
lrc,
tau,
load_weight=True):
""" Initialization
"""
# Environment and A2C parameters
self.action_dim = action_dim
self.state_dim = state_dim
self.batch_size = batch_size
self.step = step
self.gamma = gamma
self.lra = lra
self.lrc = lrc
self.tau = tau
self.episode = episode
self.train_indicator = train_indicator
# Create actor and critic networks
self.actor = Actor(state_dim, action_dim, batch_size, lra, tau)
self.critic = Critic(state_dim, action_dim, batch_size, lrc, tau)
self.buffer = MemoryBuffer(buffer_size)
# !: weights folder need to be specified & ensure only one set of A&C weights are in this folder
self.weights_dir_path = os.getcwd() + r"\saved_model\*.h5"
if load_weight:
try:
weights_actor_path = ""
weights_critic_path = ""
weights_file_path = glob.glob(self.weights_dir_path)
for file_path in weights_file_path:
if file_path.find("actor") < 0:
weights_critic_path = file_path
if file_path.find("critic") < 0:
weights_actor_path = file_path
self.load_weights(weights_actor_path, weights_critic_path)
print("")
print("Actor-Critic Models are loaded with weights...")
print("")
except:
print("")
print(
"Weights are failed to be loaded, please check weights loading path..."
)
print("")
def policy_action(self, s):
""" Use the actor to predict value
"""
return self.actor.predict(s)[0]
def bellman(self, rewards, q_values, dones):
""" Use the Bellman Equation to compute the critic target (one action only)
"""
critic_target = np.asarray(q_values)
for i in range(q_values.shape[0]):
if dones[i]:
critic_target[i] = rewards[i]
else:
critic_target[i] = rewards[i] + self.gamma * q_values[i]
return critic_target
def memorize(self, state_old, action, reward, done, state_new):
""" Store experience in memory buffer
"""
self.buffer.memorize(state_old, action, reward, done, state_new)
def sample_batch(self, batch_size):
return self.buffer.sample_batch(batch_size)
def update_models(self, states, actions, critic_target):
""" Update actor and critic networks from sampled experience
"""
# Train critic
self.critic.train_on_batch(states, actions, critic_target)
# Q-Value Gradients under Current Policy
actions = self.actor.model.predict(states)
grads = self.critic.gradients(states, actions)
# Train actor
self.actor.train(states, actions,
np.array(grads).reshape((-1, self.action_dim)))
# Transfer weights to target networks at rate Tau
self.actor.transfer_weights()
self.critic.transfer_weights()
def run(self, env):
# First, gather experience
for e in range(self.episode):
# Reset episode
# set initial state
loss, cumul_reward, cumul_loss = 0, 0, 0
done = False
state_old = env.get_vissim_state(
1, 180 * 5, [45, 55, 60, 65, 70, 75, 80
]) #TODO: make sure states are recieved correctly
actions, states, rewards = [], [], []
print("Episode: ", e, " ========================:")
for t in range(self.step):
action_original = self.policy_action(state_old)
#TODO: OU function params?
noise = OrnsteinUhlenbeckProcess(x0=action_original,
size=self.action_dim)
# action = action_orig + noise
action = noise.apply_ou(t)
# adjust too-low or too-high action
adj_action = np.zeros(len(action))
for index, value in enumerate(action):
adj_action[index] = clip(value, -1, 1)
#action_mapping function
transformed_action = Transformation.convert_actions(adj_action)
reward, state_new = env.get_vissim_reward(
180 * 5, transformed_action)
# TODO: if we know what the optimal discharging rate, then we set that as done
if t == self.step - 1: #we consider the manually setted last step as done
done = True
# ======================================================================================= Training section
if (self.train_indicator):
# Add outputs to memory buffer
self.memorize(state_old, adj_action, reward, done,
state_new)
# Sample experience from buffer
states_old, actions, rewards, dones, states_new = self.sample_batch(
self.batch_size)
# Predict target q-values using target networks
q_values = self.critic.target_predict(
[states_new,
self.actor.target_predict(states_new)])
# Compute critic target
critic_target = self.bellman(rewards, q_values, dones)
# Train both networks on sampled batch, update target networks
self.update_models(states_old, actions, critic_target)
# calculate loss
loss = self.critic.train_on_batch(states_old, actions,
critic_target)
state_old = state_new
cumul_reward += reward
cumul_loss += loss
# =======================================================================================
# ======================================================================================= report
print("|---> Step: ", t, " | Action: ", transformed_action,
" | Reward: ", reward, " | Loss: ", loss)
# =======================================================================================
# ======================================================================================= save model
if np.mod(e, 10) == 0:
print("====================> Saving model...")
self.save_weights("./saved_model/")
"""
with open("actormodel.json", "w") as outfile:
json.dump(actor.model.to_json(), outfile)
with open("criticmodel.json", "w") as outfile:
json.dump(critic.model.to_json(), outfile)
"""
# ======================================================================================= save model
print("")
print("*-------------------------------------------------*")
print("Average Accumulated Reward: " +
str(cumul_reward / self.step))
print("Average Accumulated Loss: " + str(cumul_loss / self.step))
print("*-------------------------------------------------*")
print("")
# garbage recycling
gc.collect()
def save_weights(self, path):
t = datetime.datetime.now()
time = "_" + str(t.date()) + "_" + str(t.hour) + "h-" + str(
t.minute) + "m"
path_actor = path + '_LR_{}'.format(self.lra) + time
path_critic = path + '_LR_{}'.format(self.lrc) + time
self.actor.save(path_actor)
self.critic.save(path_critic)
def load_weights(self, path_actor, path_critic):
self.actor.load(path_actor)
self.critic.load(path_critic)
class DDQN:
""" Deep Q-Learning Main Algorithm
"""
def __init__(self, action_dim, state_dim, args, input_size, hp,
export_path, env):
""" Initialization
"""
self.export_path = export_path
# Environment and DDQN parameters
self.with_per = args.with_per
self.action_dim = action_dim
self.state_dim = (args.consecutive_frames, ) + state_dim
#
self.lr = hp["lr"]
self.gamma = 0.99
# Exploration parameters for epsilon greedy strategy
self.explore_start = self.epsilon = 1.0 # exploration probability at start
self.explore_stop = 0.1 # minimum exploration probability
self.decay_rate = 0.000001 # exponential decay rate for exploration prob
self.buffer_size = 20000
self.input_size = input_size
self.video_dir = args.video_dir
# Create actor and critic networks
self.agent = Agent(self.state_dim, action_dim, self.lr, args.dueling,
input_size, args.load)
# Memory Buffer for Experience Replay
self.buffer = MemoryBuffer(self.buffer_size, args.with_per)
try:
# Init buffer
threads = 16
p = Pool(processes=threads)
while self.buffer.size() < self.buffer_size:
# Set up threaded frame accumulation
buffers = p.map_async(init_buffer, [env] * threads)
datas = buffers.get()
# Record in global memory
for data in datas:
for entry in data:
self.memorize(*entry)
# Mitigate memory leak
del buffers
del datas
print("Buffer size: {}".format(self.buffer.size()))
except KeyboardInterrupt:
p.close()
p.join()
p.close()
p.join()
# Train on pure randomness for a while
tqdm_e = tqdm(range(2000), desc='Score', leave=True, unit=" episodes")
for e in tqdm_e:
record = False
if e % 100 == 0: record = True
self.train_agent(args.batch_size, record)
if e % 1000 == 0:
self.agent.transfer_weights()
# Display score
tqdm_e.refresh()
def policy_action(self, s):
""" Apply an espilon-greedy policy to pick next action
"""
if np.random.random() <= self.epsilon:
return np.random.randint(self.action_dim)
else:
a_vect = self.agent.predict(s)[0]
return np.argmax(a_vect)
def train_agent(self, batch_size, record=False):
""" Train Q-network on batch sampled from the buffer
"""
# Sample experience from memory buffer (optionally with PER)
s, a, r, d, new_s, idx = self.buffer.sample_batch(batch_size)
# Apply Bellman Equation on batch samples to train our DDQN
q = self.agent.predict(s)
next_q = self.agent.predict(new_s)
q_targ = self.agent.target_predict(new_s)
for i in range(s.shape[0]):
old_q = q[i, a[i]]
if d[i]:
q[i, a[i]] = r[i]
else:
next_best_action = np.argmax(next_q[i, :])
q[i, a[i]] = r[i] + self.gamma * q_targ[i, next_best_action]
# Train on batch
self.agent.fit(s, q, record=record)
def train(self, env, args):
""" Main DDQN Training Algorithm
"""
results = []
tqdm_e = tqdm(range(args.nb_episodes),
desc='Score',
leave=True,
unit=" episodes")
decay_step = 0
self.t = 0
for e in tqdm_e:
# Reset episode
time, cumul_reward, cumul_r_r, done = 0, 0, 0, False
position = deque(maxlen=50)
position.append(0)
old_state = env.reset()
while not done:
decay_step += 1
env.render()
# Actor picks an action (following the policy)
a = self.policy_action(old_state)
# Retrieve new state, reward, and whether the state is terminal
new_state, r, done, _ = env.step(a)
# Memorize for experience replay
if r == 0: r_r = 0
elif r > 0: r_r = 1
else: r_r = -1
# Reward for not staying in place
if a == 2: position.append(position[-1] + 1)
if a == 3: position.append(position[-1] - 1)
r_w = abs(max(position) - min(position)) / 10000
r_r += r_w
self.memorize(old_state, a, r_r, done, new_state)
# Update current state
old_state = new_state
cumul_reward += r
cumul_r_r += r_r
time += 1
self.epsilon = self.explore_stop + (
self.explore_start - self.explore_stop) * np.exp(
-self.decay_rate * decay_step)
# Train DDQN
if (self.buffer.size() >
args.batch_size) and self.t % 2000 == 0:
self.train_agent(args.batch_size)
self.t += 1
if self.t % 10000 == 0:
self.agent.transfer_weights()
if e % 50 == 0:
self.agent.save("./model.h5")
wandb.save("./model.h5")
if e % 100 == 0:
# wandb logging
evaluate(cumul_reward, self.epsilon)
self.train_agent(args.batch_size, record=True)
# Display score
text = "Score: {}, Fake Score: {:.2f}".format(
str(cumul_reward), cumul_r_r)
tqdm_e.set_description(text)
tqdm_e.refresh()
# render gameplay video
if (e % 50 == 0):
mp4list = glob.glob('video/' + self.video_dir + '/*.mp4')
if len(mp4list) > 0:
mp4 = mp4list[-1]
video = io.open(mp4, 'r+b').read()
encoded = base64.b64encode(video)
# log gameplay video in wandb
wandb.log(
{"gameplays": wandb.Video(mp4, fps=4, format="gif")})
return results
def memorize(self, state, action, reward, done, new_state):
""" Store experience in memory buffer
"""
if (self.with_per):
q_val = self.agent.predict(state)
q_val_t = self.agent.target_predict(new_state)
next_best_action = np.argmax(q_val)
new_val = reward + self.gamma * q_val_t[0, next_best_action]
td_error = abs(new_val - q_val)[0]
else:
td_error = 0
self.buffer.memorize(state, action, reward, done, new_state, td_error)
def save(self, path):
self.agent.save(path)
def load_weights(self, path):
self.agent.load_weights(path)
class td3Agent():
"""Twin Delayed Deep Deterministic Policy Gradient(TD3) Agent
"""
def __init__(self,
env_,
is_discrete=False,
batch_size=100,
w_per=True,
update_delay=2):
# gym environments
self.env = env_
self.discrete = is_discrete
self.obs_dim = env_.observation_space.shape[0]
self.act_dim = env_.action_space.n if is_discrete else env_.action_space.shape[
0]
self.action_bound = (env_.action_space.high - env_.action_space.low
) / 2 if not is_discrete else 1.
self.action_shift = (env_.action_space.high + env_.action_space.low
) / 2 if not is_discrete else 0.
# initialize actor & critic and its targets
self.discount_factor = 0.99
self.actor = ActorNet(self.obs_dim,
self.act_dim,
self.action_bound,
lr_=3e-4,
tau_=5e-3)
self.critic = CriticNet(self.obs_dim,
self.act_dim,
lr_=3e-4,
tau_=5e-3,
discount_factor=self.discount_factor)
# Experience Buffer
self.buffer = MemoryBuffer(BUFFER_SIZE, with_per=w_per)
self.with_per = w_per
self.batch_size = batch_size
# OU-Noise-Process
self.noise = OrnsteinUhlenbeckProcess(size=self.act_dim)
# for Delayed Policy Update
self._update_step = 0
self._target_update_interval = update_delay
###################################################
# Network Related
###################################################
def make_action(self, obs, t, noise=True):
""" predict next action from Actor's Policy
"""
action_ = self.actor.predict(obs)[0]
sigma = 0.1 # std of gaussian
a = np.clip(
action_ +
np.random.normal(0, self.action_bound * sigma) if noise else 0,
-self.action_bound, self.action_bound)
#a = np.clip(action_ + self.noise.generate(t) if noise else 0, -self.action_bound, self.action_bound)
return a
def make_target_action(self, obs, noise=True):
""" predict next action from Actor's Target Policy
"""
action_ = self.actor.target_predict(obs)
sigma = 0.2
#return action_
cliped_noise = np.clip(np.random.normal(0, self.action_bound * sigma),
-self.action_bound * 0.5,
self.action_bound * 0.5)
a = np.clip(action_ + cliped_noise if noise else 0, -self.action_bound,
self.action_bound)
return a
def update_networks(self, obs, acts, critic_target):
""" Train actor & critic from sampled experience
"""
# update critic
self.critic.train(obs, acts, critic_target)
if self._update_step % self._target_update_interval == 0:
# update actor
self.actor.train(obs, self.critic.network_1)
# update target networks
self.actor.target_update()
self.critic.target_update()
self._update_step = self._update_step + 1
def train(self):
if self.with_per and (self.buffer.size() <= self.batch_size): return
# sample from buffer
states, actions, rewards, dones, new_states, idx = self.sample_batch(
self.batch_size)
# get target q-value using target network
new_actions = self.make_target_action(new_states)
q1_vals = self.critic.target_network_1.predict(
[new_states, new_actions])
q2_vals = self.critic.target_network_2.predict(
[new_states, new_actions])
# bellman iteration for target critic value
q_vals = np.min(np.vstack([q1_vals.transpose(),
q2_vals.transpose()]),
axis=0)
critic_target = np.asarray(q_vals)
# print(np.vstack([q1_vals.transpose(),q2_vals.transpose()]))
# print(q_vals)
for i in range(q1_vals.shape[0]):
if dones[i]:
critic_target[i] = rewards[i]
else:
critic_target[
i] = self.discount_factor * q_vals[i] + rewards[i]
if self.with_per:
self.buffer.update(idx[i], abs(q_vals[i] - critic_target[i]))
# train(or update) the actor & critic and target networks
self.update_networks(states, actions, critic_target)
####################################################
# Buffer Related
####################################################
def memorize(self, obs, act, reward, done, new_obs):
"""store experience in the buffer
"""
if self.with_per:
# not implemented for td3, yet.
q_val = self.critic.network(
[np.expand_dims(obs, axis=0),
self.actor.predict(obs)])
next_action = self.actor.target_network.predict(
np.expand_dims(new_obs, axis=0))
q_val_t = self.critic.target_network.predict(
[np.expand_dims(new_obs, axis=0), next_action])
new_val = reward + self.discount_factor * q_val_t
td_error = abs(new_val - q_val)[0]
else:
td_error = 0
self.buffer.memorize(obs, act, reward, done, new_obs, td_error)
def sample_batch(self, batch_size):
""" Sampling from the batch
"""
return self.buffer.sample_batch(batch_size)
###################################################
# Save & Load Networks
###################################################
def save_weights(self, path):
""" Agent's Weights Saver
"""
self.actor.save_network(path)
self.critic.save_network(path)
def load_weights(self, pretrained):
""" Agent's Weights Loader
"""
self.actor.load_network(pretrained)
self.critic.load_network(pretrained)