我想用以下方法做一个简单的BC实验 RL-教练 和一个定制的 健身房 的环境。基于这样的理念 这个 我正试图预测 目标 根据 f_1, f_2 和 f_3. 我使用的健身房环境是。
import random
import gym
from gym import spaces
import pandas as pd
import numpy as np
class TestEnvOne(gym.Env):
def __init__(self, max_time):
super(TestEnvOne, self).__init__()
self.max_time = max_time
f_1 = np.sin(np.arange(self.max_time))
f_2 = np.cos(np.arange(self.max_time))
f_3 = np.tan(np.arange(self.max_time))
target = np.roll(f_1, 1) + np.roll(f_2, 2) + np.roll(f_3, 3)
self.df = pd.DataFrame({'target': target, 'f_1': f_1, 'f_2': f_2, 'f_3': f_3})
self.max_target = self.df.max()['target']
self.min_target = self.df.min()['target']
self.max_f_1 = self.df.max()['f_1']
self.max_f_2 = self.df.max()['f_2']
self.max_f_3 = self.df.max()['f_3']
self.min_f_1 = self.df.min()['f_1']
self.min_f_2 = self.df.min()['f_2']
self.min_f_3 = self.df.min()['f_3']
self.start_step = 0
self.current_step = 0
# Actions
self.action_space = spaces.Box(
low=np.array([0, ]), high=np.array([1, ]), dtype=np.float32)
# Observation
self.observation_space = gym.spaces.dict.Dict({'measurements':
spaces.Box(low=np.array([0, 0, 0]), high=np.array([1, 1, 1]),
dtype=np.float32),
'desired_goal': spaces.Box(low=np.array([0]), high=np.array([1]),
dtype=np.float32)
})
self.reward_range = (-1, 1)
def _next_observation(self):
# Scale to between 0-1
frame = np.array([
self.df.loc[self.current_step, 'target'] / self.max_target,
self.df.loc[self.current_step, 'f_1'] / self.max_f_1,
self.df.loc[self.current_step, 'f_2'] / self.max_f_2,
self.df.loc[self.current_step, 'f_3'] / self.max_f_3,
])
frame = {'desired_goal': self.df.loc[self.current_step, 'target'] / self.max_target,
'measurements': [
self.df.loc[self.current_step, 'f_1'] / self.max_f_1,
self.df.loc[self.current_step, 'f_2'] / self.max_f_2,
self.df.loc[self.current_step, 'f_3'] / self.max_f_3
]}
return frame
def step(self, action):
self.current_step += 1
if self.current_step >= len(self.df.loc[:, 'target'].values):
self.current_step = 0
obs = self._next_observation()
reward = obs['desired_goal'] - action[0]
done = (self.current_step == self.start_step)
return {'measurements': obs['measurements'], 'desired_goal': obs['desired_goal']}, reward, done, {}
def reset(self):
# Set the current step to a random point within the data frame
self.start_step = random.randint(
0, len(self.df.loc[:, 'target'].values) - 1)
self.current_step = self.start_step
return self._next_observation()
def render(self, mode='human', close=False):
# Render the environment to the screen
print(f'Step: {self.current_step}')
print(f'Diff: {self.diff}')
print(f'Target: {self.df.loc[self.current_step, "target"]}')
我使用的预设是基于 厄运基本BC 为以下内容。
from rl_coach.agents.bc_agent import BCAgentParameters
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
from rl_coach.environments.gym_environment import GymVectorEnvironment
from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
from rl_coach.graph_managers.graph_manager import ScheduleParameters
from rl_coach.memories.memory import MemoryGranularity
####################
# Graph Scheduling #
####################
schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(2000)
schedule_params.steps_between_evaluation_periods = TrainingSteps(20)
schedule_params.evaluation_steps = EnvironmentEpisodes(5)
schedule_params.heatup_steps = EnvironmentSteps(10)
#########
# Agent #
#########
agent_params = BCAgentParameters()
agent_params.network_wrappers['main'].learning_rate = 0.00025
agent_params.memory.max_size = (MemoryGranularity.Transitions, 1000000)
agent_params.algorithm.discount = 0.99
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(0)
#agent_params.network_wrappers['main'].batch_size = 1
agent_params.network_wrappers['main'].input_embedders_parameters = {'measurements': InputEmbedderParameters(),'desired_goal': InputEmbedderParameters()}
###############
# Environment #
###############
#envPath = 'env.TestEnvZero:TestEnvZero'
envPath = 'env.TestEnvOne:TestEnvOne'
env_params = GymVectorEnvironment(level=envPath)
env_params.additional_simulator_parameters = {'max_time': 2000}
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test_using_a_trace_test = False
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
schedule_params=schedule_params, vis_params=VisualizationParameters(),
preset_validation_params=preset_validation_params)
当我运行以下命令时
coach -p presets/PruebaPresetBC.py
结果是
Please enter an experiment name: Test1
Creating graph - name: BasicRLGraphManager
Creating agent - name: agent
simple_rl_graph: Starting heatup
2020-04-30-16:21:37.128831 Heatup - Name: main_level/agent Worker: 0 Episode: 1 Total reward: -998.28 Exploration: [0.1] Steps: 2000 Training iteration: 0
Starting to improve simple_rl_graph task index 0
Traceback (most recent call last):
File "/home/user/coach_env/bin/coach", line 8, in <module>
sys.exit(main())
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/coach.py", line 777, in main
launcher.launch()
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/coach.py", line 226, in launch
self.run_graph_manager(graph_manager, args)
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/coach.py", line 612, in run_graph_manager
self.start_single_threaded(task_parameters, graph_manager, args)
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/coach.py", line 674, in start_single_threaded
start_graph(graph_manager=graph_manager, task_parameters=task_parameters)
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/coach.py", line 88, in start_graph
graph_manager.improve()
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/graph_managers/graph_manager.py", line 547, in improve
self.train_and_act(self.steps_between_evaluation_periods)
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/graph_managers/graph_manager.py", line 482, in train_and_act
self.train()
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/graph_managers/graph_manager.py", line 408, in train
[manager.train() for manager in self.level_managers]
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/graph_managers/graph_manager.py", line 408, in <listcomp>
[manager.train() for manager in self.level_managers]
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/level_manager.py", line 187, in train
[agent.train() for agent in self.agents.values()]
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/level_manager.py", line 187, in <listcomp>
[agent.train() for agent in self.agents.values()]
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/agents/agent.py", line 741, in train
total_loss, losses, unclipped_grads = self.learn_from_batch(batch)
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/agents/bc_agent.py", line 77, in learn_from_batch
targets)
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/architectures/network_wrapper.py", line 171, in train_and_sync_networks
importance_weights=importance_weights, no_accumulation=True)
File "/home/user/coach_env/lib/python3.6/site-packages/rl_coach/architectures/tensorflow_components/architecture.py", line 365, in accumulate_gradients
result = self.sess.run(fetches, feed_dict=feed_dict)
File "/home/user/coach_env/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 950, in run
run_metadata_ptr)
File "/home/user/coach_env/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1149, in _run
str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (32,) for Tensor 'main_level/agent/main/online/network_0/desired_goal/desired_goal:0', which has shape '(?, 1)'
--------------------------------
Results stored at: ./experiments/Test1/30_4_2020-16_21_0
Total runtime: 0:00:06.481459
--------------------------------
我发现32号的形状与之有关
agent_params.network_wrappers['main'].batch_size
在预设中。但我不知道如何继续，也不知道如何解决这个问题。有时使用相同的代码会出现以下异常。
ValueError: Cannot feed value of shape (32, 3) for Tensor 'main_level/agent/main/online/network_0/measurements/measurements:0', which has shape '(?, 0)'
任何帮助感激不尽。
更新2020-05-04。
根据@MarcusRenshaw的建议，我在健身房的步骤和复位功能前增加了一个打印功能。就在错误之前，它调用重置函数，观察空间是。
reset: {'desired_goal': 0.00559788442127721, 'measurements': [0.6832680466354063, 0.7301735609948197, 0.00400035745607452]}
观察空间是一样的 形状(32，3) 和 形状(32,) 误差。在加热过程中，步进函数的最后一个观测空间是
step: {'measurements': [-0.6434517999514073, 0.7654916425445919, -0.00359343140212023], 'desired_goal': -0.010710469493505773}
希望能帮到你。