强化学习框架RLlib教程003:Training APIs的使用(二)基础pythonAPI

目录

  基础pythonAPI概览

  计算动作(Computing Actions)

  获取策略状态(Accessing Policy State)

  获取模型状态(Accessing Model State)

  例子:预处理喂给model的观测值

  例子:查询一个policy的动作分布

  例子:从DQN模型中获取Q-value

  参考资料


基础pythonAPI概览

python的API可以让我们构建更多RL模型以适应更多场景。常用的RLlib API有custom environments, preprocessors, or models

这里有一个基础的使用案例:(for a more complete example, see custom_env.py)

import os
os.environ["CUDA_VISIBLE_DEVICES"] = '3'

import ray
import ray.rllib.agents.ppo as ppo
from ray.tune.logger import pretty_print

ray.init()
config = ppo.DEFAULT_CONFIG.copy()
config["num_gpus"] = 1
config["num_workers"] = 2
config["eager"] = False
trainer = ppo.PPOTrainer(config=config, env="CartPole-v0")

# Can optionally call trainer.restore(path) to load a checkpoint.

for i in range(1000):
   # Perform one iteration of training the policy with PPO
   result = trainer.train()
   print(pretty_print(result))

   if i % 100 == 0:
       checkpoint = trainer.save()
       print("checkpoint saved at", checkpoint)

# Also, in case you have trained a model outside of ray/RLlib and have created
# an h5-file with weight values in it, e.g.
# my_keras_model_trained_outside_rllib.save_weights("model.h5")
# (see: https://keras.io/models/about-keras-models/)

# ... you can load the h5-weights into your Trainer's Policy's ModelV2
# (tf or torch) by doing:
trainer.import_model("my_weights.h5")
# NOTE: In order for this to work, your (custom) model needs to implement
# the `import_from_h5` method.
# See https://github.com/ray-project/ray/blob/master/rllib/tests/test_model_imports.py
# for detailed examples for tf- and torch trainers/models.
View Code

注意:推荐使用Tune来run RLlib的trainers,这样可以简单的管理实验和可视化。仅需要配置"run": ALG_NAME, "env": ENV_NAME参数

所有的RLlib trainer都兼容Tune API。这就使得在实验中使用Tune变得简单。例如,下面的代码就可以执行一个PPO算法的超参数扫描:

import os
os.environ["CUDA_VISIBLE_DEVICES"] = '3'
import ray
from ray import tune

ray.init()
tune.run(
    "PPO",
    stop={"episode_reward_mean": 200},
    config={
        "env": "CartPole-v0",
        "num_gpus": 0,
        "num_workers": 1,
        "lr": tune.grid_search([0.01, 0.001, 0.0001]),
        "eager": False,
    },
)
View Code

tune.run()返回一个ExperimentAnalysis 对象,可供我们对训练结果进行进一步分析,也可以用于根据checkpoint恢复智能体参数,例如:

import os

os.environ["CUDA_VISIBLE_DEVICES"] = '3'
import ray
from ray import tune
import ray.rllib.agents.ppo as ppo
from ray.tune.logger import pretty_print

ray.init()
config = {
             "env": "CartPole-v0",
             "num_gpus": 0,
             "num_workers": 1,
             "eager": False,
         }
# tune.run() allows setting a custom log directory (other than ``~/ray-results``)
# and automatically saving the trained agent
analysis = ray.tune.run(
    ppo.PPOTrainer,
    config=config,
    local_dir="./",
    stop={"episode_reward_mean": 30},
    checkpoint_at_end=True)

# list of lists: one list per checkpoint; each checkpoint list contains
# 1st the path, 2nd the metric value
checkpoints = analysis.get_trial_checkpoints_paths(
    trial=analysis.get_best_trial("episode_reward_mean"),
    metric="episode_reward_mean")
# Loading and restoring a trained agent from a checkpoint is simple:
agent = ppo.PPOTrainer(config=config)
agent.restore(checkpoints[0][0])
print(agent)
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 返回目录

计算动作(Computing Actions)

根据一个训练好的agent计算动作最简单的方法是trainer.compute_action()。这个方法预处理并过滤observation之后,传递给agent的policy。下面在一个episode里展示一个简单的测试训练过的agent的例子:

import os
os.environ["CUDA_VISIBLE_DEVICES"] = '3'
import ray
import ray.rllib.agents.ppo as ppo
from ray.tune.logger import pretty_print
import gym
ray.init()
config = ppo.DEFAULT_CONFIG.copy()
config["num_gpus"] = 0
config["num_workers"] = 1
config["eager"] = False
agent = ppo.PPOTrainer(config=config, env="CartPole-v0")

# instantiate env class
env = gym.make("CartPole-v0")

# run until episode ends
episode_reward = 0
done = False
obs = env.reset()
while not done:
    action = agent.compute_action(obs)
    obs, reward, done, info = env.step(action)
    episode_reward += reward
    print(reward)
print(episode_reward)
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 返回目录

获取策略状态(Accessing Policy State)

获取trainer的内部状态是常见的操作,比如设置或获取内部权重。在RLlib trainer的状态中,集群中多个rollout workers的状态是复制的。然而,你能容易地获取和更新状态。

你也可以获取master上的trainer的状态,通过 trainer.get_policy() or trainer.workers.local_worker(),但是要注意,更新他可能不会立即影响远端的副本(如果你设置的num_workers>0)。

import os
os.environ["CUDA_VISIBLE_DEVICES"] = '3'
import ray
import ray.rllib.agents.ppo as ppo
from ray.tune.logger import pretty_print
import gym
ray.init()
config = ppo.DEFAULT_CONFIG.copy()
config["num_workers"] = 2
trainer = ppo.PPOTrainer(config=config, env="CartPole-v0")

# Get weights of the default local policy
trainer.get_policy().get_weights()

# Same as above
trainer.workers.local_worker().policy_map["default_policy"].get_weights()

# Get list of weights of each worker, including remote replicas
trainer.workers.foreach_worker(lambda ev: ev.get_policy().get_weights())

# Same as above
trainer.workers.foreach_worker_with_index(lambda ev, i: ev.get_policy().get_weights())
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 返回目录

获取模型状态(Accessing Model State)

和获取policy状态相似,你也许想得到正在训练的神经网络模型的参考。例如,你也许想单独地预训练它,或者更新成RLlib之外的参数。想做这些的话,获取policy的model就可以了。

 返回目录

例子:预处理喂给model的观测值

 

import os
os.environ["CUDA_VISIBLE_DEVICES"] = '3'
import ray
import ray.rllib.agents.ppo as ppo
from ray.tune.logger import pretty_print
import gym
ray.init()
import gym
env = gym.make("Pong-v0")

# RLlib uses preprocessors to implement transforms such as one-hot encoding
# and flattening of tuple and dict observations.
from ray.rllib.models.preprocessors import get_preprocessor
prep = get_preprocessor(env.observation_space)(env.observation_space)
print(prep)
#<ray.rllib.models.preprocessors.GenericPixelPreprocessor object at 0x7fc4d049de80>

# Observations should be preprocessed prior to feeding into a model
print(env.reset().shape)
#(210, 160, 3)
print(prep.transform(env.reset()).shape)
#(84, 84, 3)
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 返回目录

例子:查询一个policy的动作分布

 

import os
os.environ["CUDA_VISIBLE_DEVICES"] = '3'
import ray
import numpy as np
import ray.rllib.agents.ppo as ppo
from ray.tune.logger import pretty_print
ray.init()
import gym
# Get a reference to the policy
from ray.rllib.agents.ppo import PPOTrainer
trainer = PPOTrainer(env="CartPole-v0", config={"eager": True, "num_workers": 0})
policy = trainer.get_policy()
# <ray.rllib.policy.eager_tf_policy.PPOTFPolicy_eager object at 0x7fd020165470>

# Run a forward pass to get model output logits. Note that complex observations
# must be preprocessed as in the above code block.
logits, _ = policy.model.from_batch({"obs": np.array([[0.1, 0.2, 0.3, 0.4]])})
# (<tf.Tensor: id=1274, shape=(1, 2), dtype=float32, numpy=...>, [])

# Compute action distribution given logits
policy.dist_class
# <class_object 'ray.rllib.models.tf.tf_action_dist.Categorical'>
dist = policy.dist_class(logits, policy.model)
# <ray.rllib.models.tf.tf_action_dist.Categorical object at 0x7fd02301d710>

# Query the distribution for samples, sample logps
dist.sample()
# <tf.Tensor: id=661, shape=(1,), dtype=int64, numpy=..>
dist.logp([1])
# <tf.Tensor: id=1298, shape=(1,), dtype=float32, numpy=...>

# Get the estimated values for the most recent forward pass
policy.model.value_function()
# <tf.Tensor: id=670, shape=(1,), dtype=float32, numpy=...>

policy.model.base_model.summary()
# Model: "model"
# _____________________________________________________________________
# Layer (type)               Output Shape  Param #  Connected to
# =====================================================================
# observations (InputLayer)  [(None, 4)]   0
# _____________________________________________________________________
# fc_1 (Dense)               (None, 256)   1280     observations[0][0]
# _____________________________________________________________________
# fc_value_1 (Dense)         (None, 256)   1280     observations[0][0]
# _____________________________________________________________________
# fc_2 (Dense)               (None, 256)   65792    fc_1[0][0]
# _____________________________________________________________________
# fc_value_2 (Dense)         (None, 256)   65792    fc_value_1[0][0]
# _____________________________________________________________________
# fc_out (Dense)             (None, 2)     514      fc_2[0][0]
# _____________________________________________________________________
# value_out (Dense)          (None, 1)     257      fc_value_2[0][0]
# =====================================================================
# Total params: 134,915
# Trainable params: 134,915
# Non-trainable params: 0
# _____________________________________________________________________
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 返回目录

例子:从DQN模型中获取Q-value

 

import os
os.environ["CUDA_VISIBLE_DEVICES"] = '3'
import ray
import numpy as np
import ray.rllib.agents.ppo as ppo
from ray.tune.logger import pretty_print
ray.init()
import gym
# Get a reference to the policy
# Get a reference to the model through the policy
from ray.rllib.agents.dqn import DQNTrainer
trainer = DQNTrainer(env="CartPole-v0", config={"eager": True})
model = trainer.get_policy().model
# <ray.rllib.models.catalog.FullyConnectedNetwork_as_DistributionalQModel ...>

# List of all model variables
model.variables()
# [<tf.Variable 'default_policy/fc_1/kernel:0' shape=(4, 256) dtype=float32>, ...]

# Run a forward pass to get base model output. Note that complex observations
# must be preprocessed. An example of preprocessing is examples/saving_experiences.py
model_out = model.from_batch({"obs": np.array([[0.1, 0.2, 0.3, 0.4]])})
# (<tf.Tensor: id=832, shape=(1, 256), dtype=float32, numpy=...)

# Access the base Keras models (all default models have a base)
model.base_model.summary()
# Model: "model"
# _______________________________________________________________________
# Layer (type)                Output Shape    Param #  Connected to
# =======================================================================
# observations (InputLayer)   [(None, 4)]     0
# _______________________________________________________________________
# fc_1 (Dense)                (None, 256)     1280     observations[0][0]
# _______________________________________________________________________
# fc_out (Dense)              (None, 256)     65792    fc_1[0][0]
# _______________________________________________________________________
# value_out (Dense)           (None, 1)       257      fc_1[0][0]
# =======================================================================
# Total params: 67,329
# Trainable params: 67,329
# Non-trainable params: 0
# ______________________________________________________________________________

# Access the Q value model (specific to DQN)
model.get_q_value_distributions(model_out)
# [<tf.Tensor: id=891, shape=(1, 2)>, <tf.Tensor: id=896, shape=(1, 2, 1)>]

model.q_value_head.summary()
# Model: "model_1"
# _________________________________________________________________
# Layer (type)                 Output Shape              Param #
# =================================================================
# model_out (InputLayer)       [(None, 256)]             0
# _________________________________________________________________
# lambda (Lambda)              [(None, 2), (None, 2, 1), 66306
# =================================================================
# Total params: 66,306
# Trainable params: 66,306
# Non-trainable params: 0
# _________________________________________________________________

# Access the state value model (specific to DQN)
model.get_state_value(model_out)
# <tf.Tensor: id=913, shape=(1, 1), dtype=float32>

model.state_value_head.summary()
# Model: "model_2"
# _________________________________________________________________
# Layer (type)                 Output Shape              Param #
# =================================================================
# model_out (InputLayer)       [(None, 256)]             0
# _________________________________________________________________
# lambda_1 (Lambda)            (None, 1)                 66049
# =================================================================
# Total params: 66,049
# Trainable params: 66,049
# Non-trainable params: 0
# _________________________________________________________________
View Code

 

 返回目录

参考资料

https://docs.ray.io/en/latest/rllib.html

 

 返回目录

posted @ 2020-10-04 22:22  黎明程序员  阅读(981)  评论(0编辑  收藏  举报