def __init__(self, obs_dim, act_dim, max_action, gamma, tau, actor_lr,
critic_lr):
model = Model(act_dim, max_action)
self.alg = DDPG(model, gamma, tau, actor_lr, critic_lr)
self.obs_dim = obs_dim
self.act_dim = act_dim
super(Agent, self).__init__(self.alg)
# 注意：最开始先同步self.model和self.target_model的参数.
self.alg.sync_target(decay=0)
def main():
# 创建BipedalWalker环境
env = gym.make("BipedalWalkerHardcore-v3")
env.reset()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
# 使用parl框架搭建Agent：QuadrotorModel, DDPG, QuadrotorAgent三者嵌套
model = BipedalWalkerModel(act_dim)
algorithm = DDPG(model,
gamma=GAMMA,
tau=TAU,
actor_lr=ACTOR_LR,
critic_lr=CRITIC_LR)
agent = BipedalWalkerAgent(algorithm, obs_dim, act_dim)
if os.path.exists('model_dir5/steps_10075.ckpt'):
agent.restore('model_dir5/steps_10075.ckpt')
print("restore succeed")
# parl库也为DDPG算法内置了ReplayMemory，可直接从 parl.utils 引入使用
rpm = ReplayMemory(int(MEMORY_SIZE), obs_dim, act_dim)
test_flag = 0
total_steps = 0
best_reward = -float('inf')
while total_steps < TRAIN_TOTAL_STEPS:
train_reward, steps = run_episode(env, agent, rpm)
total_steps += steps
#logger.info('Steps: {} Reward: {}'.format(total_steps, train_reward))
if total_steps // TEST_EVERY_STEPS >= test_flag:
while total_steps // TEST_EVERY_STEPS >= test_flag:
test_flag += 1
evaluate_reward = evaluate(env, agent)
logger.info('Steps {}, Test reward: {}'.format(
total_steps, evaluate_reward))
if evaluate_reward >= best_reward:
best_reward = evaluate_reward
# 保存模型
ckpt = 'model_dir5/steps_{}_reward_{}.ckpt'.format(
total_steps, round(best_reward, 2))
agent.save(ckpt)
# 保存模型
ckpt = 'model_dir5/steps_{}_reward_{}.ckpt'.format(total_steps,
evaluate_reward)
agent.save(ckpt)
def main():
# 创建飞行器环境
env = make_env("Quadrotor", task="velocity_control", seed=1)
env.reset()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0] + 1
model = QuadrotorModel(act_dim)
algorithm = DDPG(model,
gamma=GAMMA,
tau=TAU,
actor_lr=ACTOR_LR,
critic_lr=CRITIC_LR)
agent = QuadrotorAgent(algorithm, obs_dim, act_dim)
ckpt = 'steps_490883_reward_-20.52.ckpt'
agent.restore(ckpt)
evaluate_reward = evaluate(env, agent, True)
logger.info('Evaluate reward: {}'.format(evaluate_reward)) # 打印评估的reward
def main():
# 创建飞行器环境
env = make_env("Quadrotor", task="no_collision", seed=1)
env.reset()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0] + 1
model = QuadrotorModel(act_dim)
algorithm = DDPG(model,
gamma=GAMMA,
tau=TAU,
actor_lr=ACTOR_LR,
critic_lr=CRITIC_LR)
agent = QuadrotorAgent(algorithm, obs_dim, act_dim)
ckpt = 'steps_970464_reward_467.17.ckpt'
agent.restore(ckpt)
evaluate_reward = evaluate(env, agent, render=True)
logger.info('Evaluate reward: {}'.format(evaluate_reward))
def main():
# 创建环境
env = Paddle()
np.random.seed(0)
act_dim = 3
obs_dim = 5
# 使用PARL框架创建agent
model = Model(act_dim)
algorithm = DDPG(model, gamma=GAMMA, tau=TAU, actor_lr=ACTOR_LR, critic_lr=CRITIC_LR)
agent = Agent(algorithm, obs_dim, act_dim)
# 加载模型
if os.path.exists('./model.ckpt'):
save_path = './model.ckpt'
agent.restore(save_path)
print("模型加载成功")
# 创建经验池
rpm = ReplayMemory(MEMORY_SIZE)
# 往经验池中预存数据
while len(rpm) < MEMORY_WARMUP_SIZE:
run_episode(agent, env, rpm)
episode = 0
while episode < TRAIN_EPISODE:
for i in range(50):
total_reward = run_episode(agent, env, rpm)
episode += 1
eval_reward = evaluate(env, agent, render=False)
logger.info('episode:{} test_reward:{}'.format(
episode, eval_reward))
save_path = './model/dqn_model_{}_{}.ckpt'.format(i, total_reward)
agent.save(save_path)
# 保存模型到文件 ./model.ckpt
agent.save('./model.ckpt')
def main():
# 创建环境
traci.start([sumoBinary, "-c", "data/cross.sumocfg",
"--tripinfo-output", "tripinfo.xml"])
traci.trafficlight.setPhase("0", 0)
act_dim = 2
obs_dim = 1440 # (10, 24, 6)
# 使用PARL框架创建agent
model = Model(act_dim)
algorithm = DDPG(model, gamma=GAMMA, tau=TAU, actor_lr=ACTOR_LR, critic_lr=CRITIC_LR)
agent = Agent(algorithm, obs_dim, act_dim)
# 加载模型
if os.path.exists('./model.ckpt'):
save_path = './model.ckpt'
agent.restore(save_path)
print("模型加载成功")
env = 0
# 创建经验池
rpm = ReplayMemory(MEMORY_SIZE)
# 往经验池中预存数据
while len(rpm) < MEMORY_WARMUP_SIZE:
save_data(agent, env, rpm)
episode = 0
while episode < TRAIN_EPISODE:
for i in range(50):
total_reward, steps = run_episode(agent, env, rpm)
episode += 1
eval_reward = evaluate(env, agent, render=False)
logger.info('episode:{} test_reward:{}'.format(
episode, eval_reward))
save_path = './model/model_{}_{}.ckpt'.format(i, total_reward)
agent.save(save_path)
# 保存模型到文件 ./model.ckpt
agent.save('./model.ckpt')
def main():
# 创建BipedalWalker环境
env = gym.make("BipedalWalker-v3")
env.reset()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
# 使用parl框架搭建Agent：QuadrotorModel, DDPG, QuadrotorAgent三者嵌套
model = BipedalWalkerModel(act_dim)
algorithm = DDPG(model,
gamma=GAMMA,
tau=TAU,
actor_lr=ACTOR_LR,
critic_lr=CRITIC_LR)
agent = BipedalWalkerAgent(algorithm, obs_dim, act_dim)
ckpt = 'model.ckpt'
agent.restore(ckpt)
evaluate(env, agent, False)
def main():
# 创建飞行器环境
env = make_env("Quadrotor_hovering_control", task="hovering_control")
env.reset()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
print(obs_dim, act_dim)
model = QuadrotorModel(act_dim + 1)
algorithm = DDPG(model,
gamma=GAMMA,
tau=TAU,
actor_lr=ACTOR_LR,
critic_lr=CRITIC_LR)
agent = QuadrotorAgent(algorithm, obs_dim, act_dim + 1)
ckpt = 'steps_700176.ckpt' # 请设置ckpt为你训练中效果最好的一次评估保存的模型文件名称
agent.restore(ckpt)
evaluate_reward = evaluate(env, agent)
logger.info('Evaluate reward: {}'.format(evaluate_reward)) # 打印评估的reward
def stock_trade(stock_file, mode_path):
day_profits = []
df = pd.read_csv(stock_file)
df = df.sort_values('date')
# The algorithms require a vectorized environment to run
env = StockTradingEnv(df)
act_dim = env.action_space.shape[0]
obs_dim = env.observation_space.shape[0]
# 使用parl框架搭建Agent：QuadrotorModel, DDPG, QuadrotorAgent三者嵌套
model = StockModel(act_dim)
algorithm = DDPG(model,
gamma=GAMMA,
tau=TAU,
actor_lr=ACTOR_LR,
critic_lr=CRITIC_LR)
agent = StockAgent(algorithm, obs_dim, act_dim)
df_test = pd.read_csv(stock_file.replace('train', 'test'))
# 加载模型
if os.path.exists(mode_path):
agent.restore(mode_path)
env2 = StockTradingEnv(df_test)
obs = env2.reset()
for i in range(len(df_test) - 1):
batch_obs = np.expand_dims(obs, axis=0)
action = agent.predict(batch_obs.astype('float32'))
action = np.squeeze(action)
action = np.random.normal(action, 1.0)
action = np.clip(action, -1.0, 1.0) ## special
action = action_mapping(action, env2.action_space.low[0],
env2.action_space.high[0])
next_obs, reward, done, info = env2.step(action)
obs = next_obs
profit = env2.render()
day_profits.append(profit)
if done:
break
return day_profits
def main():
# 创建BipedalWalker环境
env = gym.make("BipedalWalker-v3")
env.reset()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
# 使用parl框架搭建Agent：QuadrotorModel, DDPG, QuadrotorAgent三者嵌套
model = BipedalWalkerModel(act_dim)
algorithm = DDPG(model, gamma=GAMMA, tau=TAU, actor_lr=ACTOR_LR, critic_lr=CRITIC_LR)
agent = BipedalWalkerAgent(algorithm, obs_dim, act_dim)
ckpt = 'model.ckpt'
agent.restore(ckpt)
print("restore succeed")
return
best_reward = -float('inf')
for i in range(5):
evaluate_reward, steps = evaluate(env, agent,render=False)
if evaluate_reward>best_reward:
best_reward = evaluate_reward
logger.info('Episode:{}, Evaluate reward: {}'.format(i, evaluate_reward))
print("best_reward:",best_reward)
def main():
# 创建飞行器环境
env = make_env("Quadrotor", task="hovering_control")
env.reset()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
act_dim = act_dim + 1
model = QuadrotorModel(act_dim)
algorithm = DDPG(model,
gamma=GAMMA,
tau=TAU,
actor_lr=ACTOR_LR,
critic_lr=CRITIC_LR)
agent = QuadrotorAgent(algorithm, obs_dim, act_dim)
# parl库也为DDPG算法内置了ReplayMemory，可直接从 parl.utils 引入使用
rpm = ReplayMemory(int(MEMORY_SIZE), obs_dim, act_dim)
# 启动训练
test_flag = 0
total_steps = 0
while total_steps < TRAIN_TOTAL_STEPS:
train_reward, steps = run_episode(env, agent, rpm)
total_steps += steps
logger.info('Steps: {} Reward: {}'.format(total_steps,
train_reward)) # 打印训练reward
if total_steps // TEST_EVERY_STEPS >= test_flag: # 每隔一定step数，评估一次模型
while total_steps // TEST_EVERY_STEPS >= test_flag:
test_flag += 1
evaluate_reward = evaluate(env, agent)
logger.info('Steps {}, Test reward: {}'.format(
total_steps, evaluate_reward)) # 打印评估的reward
# 每评估一次，就保存一次模型，以训练的step数命名
ckpt = 'model_dir/steps_{}.ckpt'.format(total_steps)
agent.save(ckpt)
def main():
# 创建环境
game = Pong(width=200, height=200, MAX_SCORE=11)
p = PLE(game, fps=30, display_screen=True, force_fps=False)
p.reset_game()
# 根据parl框架构建agent
print(p.getActionSet())
act_dim = len(p.getActionSet())
print("act_dim:", act_dim)
obs_dim = 200 * 200
# 使用parl框架搭建Agent：QuadrotorModel, DDPG, QuadrotorAgent三者嵌套
model = PongModel(act_dim)
algorithm = DDPG(model,
gamma=GAMMA,
tau=TAU,
actor_lr=ACTOR_LR,
critic_lr=CRITIC_LR)
agent = PongAgent(algorithm, obs_dim, act_dim)
rpm = ReplayMemory(int(MEMORY_SIZE), obs_dim, act_dim)
max_episode = 20000
# 开始训练
episode = 0
best_reward = -float('inf')
while episode < max_episode: # 训练max_episode个回合，test部分不计算入episode数量
# train part
for i in range(0, 50):
total_reward = run_episode(p, agent, rpm)
episode += 1
# test part
eval_reward = evaluate(p, agent, render=True) # render=True 查看显示效果
if eval_reward > best_reward:
best_reward = eval_reward
agent.save('model_dir/ddpg_pong_{}.ckpt'.format(episode))
logger.info('episode:{} test_reward:{}'.format(episode, eval_reward))
obs_dim = env.observation_space.shape[0]
# 创建经验池
rpm = ReplayMemory(int(MEMORY_SIZE), obs_dim, act_dim)
# 根据parl框架构建agent
######################################################################
######################################################################
#
# 4. 请参考课堂Demo，嵌套Model, DQN, Agent构建 agent
#
######################################################################
######################################################################
model = QuadrotorModel(act_dim=act_dim)
algorithm = DDPG(model,
gamma=GAMMA,
tau=TAU,
actor_lr=ACTOR_LR,
critic_lr=CRITIC_LR)
agent = Agent(algorithm, obs_dim=obs_dim, act_dim=act_dim)
# 加载模型
# save_path = './dqn_model.ckpt'
# agent.restore(save_path)
# 先往经验池里存一些数据，避免最开始训练的时候样本丰富度不够
max_episode = 500
test_flag = 0
total_steps = 0
while total_steps < TRAIN_TOTAL_STEPS:
train_reward, steps = run_episode(env, agent, rpm)
break
total_reward.append(c_r)
env_reward.append(d_r)
total_reward.append(np.mean(total_reward))
env_reward.append(np.mean(env_reward))
return total_reward, env_reward
env = make_env("Quadrotor", task="velocity_control")
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
logger.info('obs_dim:{} , act_dim:{}'.format(obs_dim, act_dim))
model = QModel(act_dim)
alg = DDPG(model, gamma=Gamma, tau=Tau, actor_lr=A_lr, critic_lr=C_lr)
agent = QAgent(alg, obs_dim, act_dim)
rpm = ReplayMemory(int(Max_Size), obs_dim, act_dim)
# loadpath = 'M1_-1027_Over.ckpt'
# if os.path.exists(loadpath):
# agent.restore(loadpath)
# logger.info('Already Loaded')
# else:
# exit(1)
test_flag = 0
total_step = 0
reward_max = -1027
while True:
class Agent(parl.Agent):
def __init__(self, obs_dim, act_dim, max_action, gamma, tau, actor_lr,
critic_lr):
model = Model(act_dim, max_action)
self.alg = DDPG(model, gamma, tau, actor_lr, critic_lr)
self.obs_dim = obs_dim
self.act_dim = act_dim
super(Agent, self).__init__(self.alg)
# 注意：最开始先同步self.model和self.target_model的参数.
self.alg.sync_target(decay=0)
def build_program(self):
self.pred_program = fluid.Program()
self.learn_program = fluid.Program()
with fluid.program_guard(self.pred_program):
obs = layers.data(name='obs',
shape=[self.obs_dim],
dtype='float32')
self.pred_act = self.alg.predict(obs)
with fluid.program_guard(self.learn_program):
obs = layers.data(name='obs',
shape=[self.obs_dim],
dtype='float32')
act = layers.data(name='act',
shape=[self.act_dim],
dtype='float32')
reward = layers.data(name='reward', shape=[], dtype='float32')
next_obs = layers.data(name='next_obs',
shape=[self.obs_dim],
dtype='float32')
terminal = layers.data(name='terminal', shape=[], dtype='bool')
self.actor_cost, self.critic_cost = self.alg.learn(
obs, act, reward, next_obs, terminal)
def predict(self, obs):
obs = np.expand_dims(obs, axis=0)
act = self.fluid_executor.run(self.pred_program,
feed={'obs': obs},
fetch_list=[self.pred_act])[0]
act = np.squeeze(act)
return act
def learn(self, obs, act, reward, next_obs, terminal):
feed = {
'obs': obs,
'act': act,
'reward': reward,
'next_obs': next_obs,
'terminal': terminal
}
[critic_cost, actor_cost] = self.fluid_executor.run(
self.learn_program,
feed=feed,
fetch_list=[self.critic_cost, self.actor_cost])
self.alg.sync_target()
return critic_cost[0], actor_cost[0]
def save_model4visual(self, dir, name):
feed_vars = ['obs', 'act', 'reward', 'next_obs', 'terminal']
target_vars = [self.actor_cost, self.critic_cost]
executor = self.fluid_executor
program = self.learn_program
fluid.io.save_inference_model(dir,
feed_vars,
target_vars,
executor,
program,
program_only=True)
def main(train=True):
if train:
# 加载数据
df = pd.read_csv('wudigushi/DATA/AAPL.csv')
df = df.sort_values('Date')
# 创建环境
env = StockTradingEnv(df)
env.reset()
act_dim = env.action_space.shape[0]
obs_dim = env.observation_space.shape[0]
print(act_dim)
print(obs_dim)
model = WudigupiaoModel(act_dim)
algorithm = DDPG(
model, gamma=GAMMA, tau=TAU, actor_lr=ACTOR_LR, critic_lr=CRITIC_LR)
agent = MyStockAgent(algorithm, obs_dim, act_dim)
rpm = ReplayMemory(int(MEMORY_SIZE), obs_dim , act_dim)
test_flag = 0
total_steps = 0
while total_steps < TRAIN_TOTAL_STEPS:
train_reward, steps = run_episode(env, agent, rpm)
total_steps += steps
# logger.info('Steps: {} Reward: {}'.format(total_steps, train_reward))
if total_steps // TEST_EVERY_STEPS >= test_flag:
# print('s1')
while total_steps // TEST_EVERY_STEPS >= test_flag:
test_flag += 1
evaluate_reward = evaluate(env, agent)
logger.info('Steps {}, Test reward: {}'.format(total_steps,
evaluate_reward))
ckpt = 'wudigushi/ckpt/steps_{}.ckpt'.format(total_steps)
agent.save(ckpt)
else:
ckpt = 'wudigushi/ckpt/steps_980117.ckpt' # 请设置ckpt为你训练中效果最好的一次评估保存的模型文件名称
df = pd.read_csv('wudigushi/DATA/AAPL.csv')
df = df.sort_values('Date')
# 创建环境
env = StockTradingEnv(df)
env.reset()
act_dim = env.action_space.shape[0]
obs_dim = env.observation_space.shape[0]
# obs_dim = 36
print(act_dim)
print(obs_dim)
model = WudigupiaoModel(act_dim)
algorithm = DDPG(
model, gamma=GAMMA, tau=TAU, actor_lr=ACTOR_LR, critic_lr=CRITIC_LR)
agent = MyStockAgent(algorithm, obs_dim, act_dim)
agent.restore(ckpt)
evaluate_reward = evaluate(env, agent)
logger.info('Evaluate reward: {}'.format(evaluate_reward)) # 打印评估的reward
