def init(point_dict):
rl_brain = PolicyGradient(
n_actions=env.action_space_num,
n_features=env.obs_num,
learning_rate=0.02,
reward_decay=0.99,
# output_graph=True,
)
rl_brain.load_model(
path='ReinforceLearning/saved_model/PG - 1/policy_gradient.ckpt')
# 所有节点共用一个RL，PID控制器是独立的
for id in point_dict:
if id != 0:
pid_brain = PID(100, 0.1, 1, 100)
e = env(point_dict[id])
a = Agent(rl_brain, pid_brain, e)
Agent_dict[id] = a
The cart pole example
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import gym
from ReinforceLearning.brain.Policy_Gradient import PolicyGradient
import matplotlib.pyplot as plt
from ReinforceLearning.environment.env import env
from simulation.output_generator import output_generator
RENDER = False # rendering wastes time
MAX_REWARD = 0
Myenv = env(max_energy=500)
output = output_generator(scale=20,
bias=20,
period=200,
phase=0,
max_iter=20000)
# output.load("imagedata.npy")
Myenv.output_need = output.output
# print(env.action_space)
# print(env.observation_space)
# print(env.observation_space.high)
# print(env.observation_space.low)
RL = PolicyGradient(
n_actions=Myenv.action_space_num,
n_features=Myenv.obs_num,
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import numpy as np
from ReinforceLearning.brain.Policy_Gradient import PolicyGradient
import matplotlib.pyplot as plt
from ReinforceLearning.environment.env import env
from simulation.output_generator import output_generator
from method.myfunc import smooth
DISPLAY_REWARD_THRESHOLD = 400 # renders environment if total episode reward is greater then this threshold
RENDER = False # rendering wastes time
Myenv = env()
output = output_generator(scale=20,
bias=20,
period=200,
phase=0,
max_iter=20000)
output.load("imagedata.npy")
Myenv.output_need = output.output
# print(env.action_space)
# print(env.observation_space)
# print(env.observation_space.high)
# print(env.observation_space.low)
RL = PolicyGradient(
n_actions=Myenv.action_space_num,
n_features=Myenv.obs_num,
def set_env(self):
self.env = env(self)