fix some errors
This commit is contained in:
qiwang067
@@ -1,75 +0,0 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# -*- coding: utf-8 -*-
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import numpy as np
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class QLearningAgent(object):
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def __init__(self,
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obs_n,
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act_n,
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learning_rate=0.01,
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gamma=0.9,
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e_greed=0.1):
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self.act_n = act_n # 动作维度，有几个动作可选
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self.lr = learning_rate # 学习率
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self.gamma = gamma # reward的衰减率
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self.epsilon = e_greed # 按一定概率随机选动作
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self.Q = np.zeros((obs_n, act_n))
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# 根据输入观察值，采样输出的动作值，带探索
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def sample(self, obs):
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if np.random.uniform(0, 1) < (1.0 - self.epsilon): #根据table的Q值选动作
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action = self.predict(obs)
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else:
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action = np.random.choice(self.act_n) #有一定概率随机探索选取一个动作
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return action
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# 根据输入观察值，预测输出的动作值
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def predict(self, obs):
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Q_list = self.Q[obs, :]
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maxQ = np.max(Q_list)
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action_list = np.where(Q_list == maxQ)[0] # maxQ可能对应多个action
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action = np.random.choice(action_list)
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return action
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# 学习方法，也就是更新Q-table的方法
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def learn(self, obs, action, reward, next_obs, done):
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""" off-policy
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obs: 交互前的obs, s_t
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action: 本次交互选择的action, a_t
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reward: 本次动作获得的奖励r
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next_obs: 本次交互后的obs, s_t+1
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done: episode是否结束
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"""
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predict_Q = self.Q[obs, action]
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if done:
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target_Q = reward # 没有下一个状态了
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else:
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target_Q = reward + self.gamma * np.max(
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self.Q[next_obs, :]) # Q-learning
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self.Q[obs, action] += self.lr * (target_Q - predict_Q) # 修正q
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# 把 Q表格 的数据保存到文件中
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def save(self):
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npy_file = './q_table.npy'
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np.save(npy_file, self.Q)
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print(npy_file + ' saved.')
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# 从文件中读取数据到 Q表格
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def restore(self, npy_file='./q_table.npy'):
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self.Q = np.load(npy_file)
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print(npy_file + ' loaded.')
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@@ -1,195 +0,0 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# -*- coding: utf-8 -*-
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import gym
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import turtle
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import numpy as np
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# turtle tutorial : https://docs.python.org/3.3/library/turtle.html
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def GridWorld(gridmap=None, is_slippery=False):
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if gridmap is None:
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gridmap = ['SFFF', 'FHFH', 'FFFH', 'HFFG']
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env = gym.make("FrozenLake-v0", desc=gridmap, is_slippery=False)
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env = FrozenLakeWapper(env)
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return env
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class FrozenLakeWapper(gym.Wrapper):
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def __init__(self, env):
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gym.Wrapper.__init__(self, env)
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self.max_y = env.desc.shape[0]
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self.max_x = env.desc.shape[1]
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self.t = None
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self.unit = 50
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def draw_box(self, x, y, fillcolor='', line_color='gray'):
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self.t.up()
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self.t.goto(x * self.unit, y * self.unit)
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self.t.color(line_color)
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self.t.fillcolor(fillcolor)
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self.t.setheading(90)
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self.t.down()
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self.t.begin_fill()
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for _ in range(4):
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self.t.forward(self.unit)
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self.t.right(90)
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self.t.end_fill()
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def move_player(self, x, y):
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self.t.up()
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self.t.setheading(90)
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self.t.fillcolor('red')
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self.t.goto((x + 0.5) * self.unit, (y + 0.5) * self.unit)
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def render(self):
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if self.t == None:
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self.t = turtle.Turtle()
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self.wn = turtle.Screen()
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self.wn.setup(self.unit * self.max_x + 100,
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self.unit * self.max_y + 100)
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self.wn.setworldcoordinates(0, 0, self.unit * self.max_x,
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self.unit * self.max_y)
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self.t.shape('circle')
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self.t.width(2)
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self.t.speed(0)
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self.t.color('gray')
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for i in range(self.desc.shape[0]):
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for j in range(self.desc.shape[1]):
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x = j
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y = self.max_y - 1 - i
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if self.desc[i][j] == b'S': # Start
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self.draw_box(x, y, 'white')
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elif self.desc[i][j] == b'F': # Frozen ice
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self.draw_box(x, y, 'white')
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elif self.desc[i][j] == b'G': # Goal
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self.draw_box(x, y, 'yellow')
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elif self.desc[i][j] == b'H': # Hole
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self.draw_box(x, y, 'black')
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else:
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self.draw_box(x, y, 'white')
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self.t.shape('turtle')
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x_pos = self.s % self.max_x
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y_pos = self.max_y - 1 - int(self.s / self.max_x)
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self.move_player(x_pos, y_pos)
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class CliffWalkingWapper(gym.Wrapper):
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def __init__(self, env):
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gym.Wrapper.__init__(self, env)
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self.t = None
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self.unit = 50
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self.max_x = 12
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self.max_y = 4
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def draw_x_line(self, y, x0, x1, color='gray'):
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assert x1 > x0
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self.t.color(color)
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self.t.setheading(0)
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self.t.up()
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self.t.goto(x0, y)
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self.t.down()
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self.t.forward(x1 - x0)
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def draw_y_line(self, x, y0, y1, color='gray'):
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assert y1 > y0
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self.t.color(color)
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self.t.setheading(90)
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self.t.up()
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self.t.goto(x, y0)
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self.t.down()
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self.t.forward(y1 - y0)
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def draw_box(self, x, y, fillcolor='', line_color='gray'):
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self.t.up()
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self.t.goto(x * self.unit, y * self.unit)
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self.t.color(line_color)
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self.t.fillcolor(fillcolor)
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self.t.setheading(90)
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self.t.down()
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self.t.begin_fill()
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for i in range(4):
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self.t.forward(self.unit)
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self.t.right(90)
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self.t.end_fill()
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def move_player(self, x, y):
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self.t.up()
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self.t.setheading(90)
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self.t.fillcolor('red')
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self.t.goto((x + 0.5) * self.unit, (y + 0.5) * self.unit)
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def render(self):
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if self.t == None:
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self.t = turtle.Turtle()
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self.wn = turtle.Screen()
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self.wn.setup(self.unit * self.max_x + 100,
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self.unit * self.max_y + 100)
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self.wn.setworldcoordinates(0, 0, self.unit * self.max_x,
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self.unit * self.max_y)
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self.t.shape('circle')
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self.t.width(2)
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self.t.speed(0)
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self.t.color('gray')
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for _ in range(2):
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self.t.forward(self.max_x * self.unit)
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self.t.left(90)
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self.t.forward(self.max_y * self.unit)
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self.t.left(90)
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for i in range(1, self.max_y):
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self.draw_x_line(
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y=i * self.unit, x0=0, x1=self.max_x * self.unit)
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for i in range(1, self.max_x):
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self.draw_y_line(
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x=i * self.unit, y0=0, y1=self.max_y * self.unit)
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for i in range(1, self.max_x - 1):
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self.draw_box(i, 0, 'black')
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self.draw_box(self.max_x - 1, 0, 'yellow')
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self.t.shape('turtle')
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x_pos = self.s % self.max_x
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y_pos = self.max_y - 1 - int(self.s / self.max_x)
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self.move_player(x_pos, y_pos)
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if __name__ == '__main__':
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# 环境1：FrozenLake, 可以配置冰面是否是滑的
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# 0 left, 1 down, 2 right, 3 up
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env = gym.make("FrozenLake-v0", is_slippery=False)
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env = FrozenLakeWapper(env)
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# 环境2：CliffWalking, 悬崖环境
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# env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left
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# env = CliffWalkingWapper(env)
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# 环境3：自定义格子世界，可以配置地图, S为出发点Start, F为平地Floor, H为洞Hole, G为出口目标Goal
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# gridmap = [
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# 'SFFF',
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# 'FHFF',
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# 'FFFF',
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# 'HFGF' ]
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# env = GridWorld(gridmap)
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env.reset()
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for step in range(10):
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action = np.random.randint(0, 4)
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obs, reward, done, info = env.step(action)
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print('step {}: action {}, obs {}, reward {}, done {}, info {}'.format(\
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step, action, obs, reward, done, info))
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# env.render() # 渲染一帧图像
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@@ -1,90 +0,0 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
|
||||
#
|
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# http://www.apache.org/licenses/LICENSE-2.0
|
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#
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# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
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# limitations under the License.
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# -*- coding: utf-8 -*-
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import gym
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from gridworld import CliffWalkingWapper, FrozenLakeWapper
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from agent import QLearningAgent
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import time
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def run_episode(env, agent, render=False):
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total_steps = 0 # 记录每个episode走了多少step
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total_reward = 0
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obs = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）
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while True:
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action = agent.sample(obs) # 根据算法选择一个动作
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next_obs, reward, done, _ = env.step(action) # 与环境进行一个交互
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# 训练 Q-learning算法
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agent.learn(obs, action, reward, next_obs, done) # 不需要下一步的action
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obs = next_obs # 存储上一个观察值
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total_reward += reward
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total_steps += 1 # 计算step数
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if render:
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env.render() #渲染新的一帧图形
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if done:
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break
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return total_reward, total_steps
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def test_episode(env, agent):
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total_reward = 0
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obs = env.reset()
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while True:
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action = agent.predict(obs) # greedy
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next_obs, reward, done, _ = env.step(action)
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total_reward += reward
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obs = next_obs
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time.sleep(0.5)
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env.render()
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if done:
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print('test reward = %.1f' % (total_reward))
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break
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def main():
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# env = gym.make("FrozenLake-v0", is_slippery=False) # 0 left, 1 down, 2 right, 3 up
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# env = FrozenLakeWapper(env)
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env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left
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env = CliffWalkingWapper(env)
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agent = QLearningAgent(
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obs_n=env.observation_space.n,
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act_n=env.action_space.n,
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learning_rate=0.1,
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gamma=0.9,
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e_greed=0.1)
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is_render = False
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for episode in range(500):
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ep_reward, ep_steps = run_episode(env, agent, is_render)
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print('Episode %s: steps = %s , reward = %.1f' % (episode, ep_steps,
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ep_reward))
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# 每隔20个episode渲染一下看看效果
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if episode % 20 == 0:
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is_render = True
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else:
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is_render = False
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# 训练结束，查看算法效果
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test_episode(env, agent)
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if __name__ == "__main__":
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main()
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@@ -1,74 +0,0 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# -*- coding: utf-8 -*-
|
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|
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import numpy as np
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# 根据Q表格选动作
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class SarsaAgent(object):
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def __init__(self,
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obs_n,
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act_n,
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learning_rate=0.01,
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gamma=0.9,
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e_greed=0.1):
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self.act_n = act_n # 动作维度，有几个动作可选
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self.lr = learning_rate # 学习率
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self.gamma = gamma # reward的衰减率
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self.epsilon = e_greed # 按一定概率随机选动作
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self.Q = np.zeros((obs_n, act_n)) # 初始化Q表格
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# 根据输入观察值，采样输出的动作值，带探索(epsilon-greedy，训练时用这个方法)
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def sample(self, obs):
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if np.random.uniform(0, 1) < (1.0 - self.epsilon): #根据table的Q值选动作
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action = self.predict(obs)
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else:
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action = np.random.choice(self.act_n) #有一定概率随机探索选取一个动作
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return action
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# 根据输入观察值，预测输出的动作值（已有里面挑最大，贪心的算法，只有利用，没有探索）
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def predict(self, obs):
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Q_list = self.Q[obs, :]
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maxQ = np.max(Q_list) # 找到最大Q对应的下标
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action_list = np.where(Q_list == maxQ)[0] # maxQ可能对应多个action
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action = np.random.choice(action_list) # 从这些action中随机挑一个action（可以打印出来看看）
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return action
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# 学习方法，也就是更新Q-table的方法
|
||||
def learn(self, obs, action, reward, next_obs, next_action, done):
|
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""" on-policy
|
||||
obs: 交互前的obs, s_t
|
||||
action: 本次交互选择的action, a_t
|
||||
reward: 本次动作获得的奖励r
|
||||
next_obs: 本次交互后的obs, s_t+1
|
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next_action: 根据当前Q表格, 针对next_obs会选择的动作, a_t+1
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done: episode是否结束
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"""
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predict_Q = self.Q[obs, action]
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||||
if done: # done为ture的话，代表这是episode最后一个状态
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target_Q = reward # 没有下一个状态了
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else:
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target_Q = reward + self.gamma * self.Q[next_obs,
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||||
next_action] # Sarsa
|
||||
self.Q[obs, action] += self.lr * (target_Q - predict_Q) # 修正q
|
||||
|
||||
def save(self):
|
||||
npy_file = './q_table.npy'
|
||||
np.save(npy_file, self.Q)
|
||||
print(npy_file + ' saved.')
|
||||
|
||||
def restore(self, npy_file='./q_table.npy'):
|
||||
self.Q = np.load(npy_file)
|
||||
print(npy_file + ' loaded.')
|
||||
@@ -1,195 +0,0 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import gym
|
||||
import turtle
|
||||
import numpy as np
|
||||
|
||||
# turtle tutorial : https://docs.python.org/3.3/library/turtle.html
|
||||
|
||||
|
||||
def GridWorld(gridmap=None, is_slippery=False):
|
||||
if gridmap is None:
|
||||
gridmap = ['SFFF', 'FHFH', 'FFFH', 'HFFG']
|
||||
env = gym.make("FrozenLake-v0", desc=gridmap, is_slippery=False)
|
||||
env = FrozenLakeWapper(env)
|
||||
return env
|
||||
|
||||
|
||||
class FrozenLakeWapper(gym.Wrapper):
|
||||
def __init__(self, env):
|
||||
gym.Wrapper.__init__(self, env)
|
||||
self.max_y = env.desc.shape[0]
|
||||
self.max_x = env.desc.shape[1]
|
||||
self.t = None
|
||||
self.unit = 50
|
||||
|
||||
def draw_box(self, x, y, fillcolor='', line_color='gray'):
|
||||
self.t.up()
|
||||
self.t.goto(x * self.unit, y * self.unit)
|
||||
self.t.color(line_color)
|
||||
self.t.fillcolor(fillcolor)
|
||||
self.t.setheading(90)
|
||||
self.t.down()
|
||||
self.t.begin_fill()
|
||||
for _ in range(4):
|
||||
self.t.forward(self.unit)
|
||||
self.t.right(90)
|
||||
self.t.end_fill()
|
||||
|
||||
def move_player(self, x, y):
|
||||
self.t.up()
|
||||
self.t.setheading(90)
|
||||
self.t.fillcolor('red')
|
||||
self.t.goto((x + 0.5) * self.unit, (y + 0.5) * self.unit)
|
||||
|
||||
def render(self):
|
||||
if self.t == None:
|
||||
self.t = turtle.Turtle()
|
||||
self.wn = turtle.Screen()
|
||||
self.wn.setup(self.unit * self.max_x + 100,
|
||||
self.unit * self.max_y + 100)
|
||||
self.wn.setworldcoordinates(0, 0, self.unit * self.max_x,
|
||||
self.unit * self.max_y)
|
||||
self.t.shape('circle')
|
||||
self.t.width(2)
|
||||
self.t.speed(0)
|
||||
self.t.color('gray')
|
||||
for i in range(self.desc.shape[0]):
|
||||
for j in range(self.desc.shape[1]):
|
||||
x = j
|
||||
y = self.max_y - 1 - i
|
||||
if self.desc[i][j] == b'S': # Start
|
||||
self.draw_box(x, y, 'white')
|
||||
elif self.desc[i][j] == b'F': # Frozen ice
|
||||
self.draw_box(x, y, 'white')
|
||||
elif self.desc[i][j] == b'G': # Goal
|
||||
self.draw_box(x, y, 'yellow')
|
||||
elif self.desc[i][j] == b'H': # Hole
|
||||
self.draw_box(x, y, 'black')
|
||||
else:
|
||||
self.draw_box(x, y, 'white')
|
||||
self.t.shape('turtle')
|
||||
|
||||
x_pos = self.s % self.max_x
|
||||
y_pos = self.max_y - 1 - int(self.s / self.max_x)
|
||||
self.move_player(x_pos, y_pos)
|
||||
|
||||
|
||||
class CliffWalkingWapper(gym.Wrapper):
|
||||
def __init__(self, env):
|
||||
gym.Wrapper.__init__(self, env)
|
||||
self.t = None
|
||||
self.unit = 50
|
||||
self.max_x = 12
|
||||
self.max_y = 4
|
||||
|
||||
def draw_x_line(self, y, x0, x1, color='gray'):
|
||||
assert x1 > x0
|
||||
self.t.color(color)
|
||||
self.t.setheading(0)
|
||||
self.t.up()
|
||||
self.t.goto(x0, y)
|
||||
self.t.down()
|
||||
self.t.forward(x1 - x0)
|
||||
|
||||
def draw_y_line(self, x, y0, y1, color='gray'):
|
||||
assert y1 > y0
|
||||
self.t.color(color)
|
||||
self.t.setheading(90)
|
||||
self.t.up()
|
||||
self.t.goto(x, y0)
|
||||
self.t.down()
|
||||
self.t.forward(y1 - y0)
|
||||
|
||||
def draw_box(self, x, y, fillcolor='', line_color='gray'):
|
||||
self.t.up()
|
||||
self.t.goto(x * self.unit, y * self.unit)
|
||||
self.t.color(line_color)
|
||||
self.t.fillcolor(fillcolor)
|
||||
self.t.setheading(90)
|
||||
self.t.down()
|
||||
self.t.begin_fill()
|
||||
for i in range(4):
|
||||
self.t.forward(self.unit)
|
||||
self.t.right(90)
|
||||
self.t.end_fill()
|
||||
|
||||
def move_player(self, x, y):
|
||||
self.t.up()
|
||||
self.t.setheading(90)
|
||||
self.t.fillcolor('red')
|
||||
self.t.goto((x + 0.5) * self.unit, (y + 0.5) * self.unit)
|
||||
|
||||
def render(self):
|
||||
if self.t == None:
|
||||
self.t = turtle.Turtle()
|
||||
self.wn = turtle.Screen()
|
||||
self.wn.setup(self.unit * self.max_x + 100,
|
||||
self.unit * self.max_y + 100)
|
||||
self.wn.setworldcoordinates(0, 0, self.unit * self.max_x,
|
||||
self.unit * self.max_y)
|
||||
self.t.shape('circle')
|
||||
self.t.width(2)
|
||||
self.t.speed(0)
|
||||
self.t.color('gray')
|
||||
for _ in range(2):
|
||||
self.t.forward(self.max_x * self.unit)
|
||||
self.t.left(90)
|
||||
self.t.forward(self.max_y * self.unit)
|
||||
self.t.left(90)
|
||||
for i in range(1, self.max_y):
|
||||
self.draw_x_line(
|
||||
y=i * self.unit, x0=0, x1=self.max_x * self.unit)
|
||||
for i in range(1, self.max_x):
|
||||
self.draw_y_line(
|
||||
x=i * self.unit, y0=0, y1=self.max_y * self.unit)
|
||||
|
||||
for i in range(1, self.max_x - 1):
|
||||
self.draw_box(i, 0, 'black')
|
||||
self.draw_box(self.max_x - 1, 0, 'yellow')
|
||||
self.t.shape('turtle')
|
||||
|
||||
x_pos = self.s % self.max_x
|
||||
y_pos = self.max_y - 1 - int(self.s / self.max_x)
|
||||
self.move_player(x_pos, y_pos)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 环境1：FrozenLake, 可以配置冰面是否是滑的
|
||||
# 0 left, 1 down, 2 right, 3 up
|
||||
env = gym.make("FrozenLake-v0", is_slippery=False)
|
||||
env = FrozenLakeWapper(env)
|
||||
|
||||
# 环境2：CliffWalking, 悬崖环境
|
||||
# env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left
|
||||
# env = CliffWalkingWapper(env)
|
||||
|
||||
# 环境3：自定义格子世界，可以配置地图, S为出发点Start, F为平地Floor, H为洞Hole, G为出口目标Goal
|
||||
# gridmap = [
|
||||
# 'SFFF',
|
||||
# 'FHFF',
|
||||
# 'FFFF',
|
||||
# 'HFGF' ]
|
||||
# env = GridWorld(gridmap)
|
||||
|
||||
env.reset()
|
||||
for step in range(10):
|
||||
action = np.random.randint(0, 4)
|
||||
obs, reward, done, info = env.step(action)
|
||||
print('step {}: action {}, obs {}, reward {}, done {}, info {}'.format(\
|
||||
step, action, obs, reward, done, info))
|
||||
# env.render() # 渲染一帧图像
|
||||
@@ -1,92 +0,0 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import gym
|
||||
from gridworld import CliffWalkingWapper, FrozenLakeWapper
|
||||
from agent import SarsaAgent
|
||||
import time
|
||||
|
||||
|
||||
def run_episode(env, agent, render=False):
|
||||
total_steps = 0 # 记录每个episode走了多少step
|
||||
total_reward = 0
|
||||
|
||||
obs = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）
|
||||
action = agent.sample(obs) # 根据算法选择一个动作
|
||||
|
||||
while True:
|
||||
next_obs, reward, done, _ = env.step(action) # 与环境进行一个交互
|
||||
next_action = agent.sample(next_obs) # 根据算法选择一个动作
|
||||
# 训练 Sarsa 算法
|
||||
agent.learn(obs, action, reward, next_obs, next_action, done)
|
||||
|
||||
action = next_action
|
||||
obs = next_obs # 存储上一个观察值
|
||||
total_reward += reward
|
||||
total_steps += 1 # 计算step数
|
||||
if render:
|
||||
env.render() #渲染新的一帧图形
|
||||
if done:
|
||||
break
|
||||
return total_reward, total_steps
|
||||
|
||||
|
||||
def test_episode(env, agent):
|
||||
total_reward = 0
|
||||
obs = env.reset()
|
||||
while True:
|
||||
action = agent.predict(obs) # greedy，只取最优的动作
|
||||
next_obs, reward, done, _ = env.step(action)
|
||||
total_reward += reward
|
||||
obs = next_obs
|
||||
time.sleep(0.5) # 每个step延迟0.5秒来看看效果
|
||||
env.render()
|
||||
if done:
|
||||
print('test reward = %.1f' % (total_reward))
|
||||
break
|
||||
|
||||
|
||||
def main():
|
||||
# env = gym.make("FrozenLake-v0", is_slippery=False) # 0 left, 1 down, 2 right, 3 up
|
||||
# env = FrozenLakeWapper(env)
|
||||
|
||||
env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left
|
||||
env = CliffWalkingWapper(env) # 这行不加也可以，这个是为了显示效果更好一点
|
||||
|
||||
agent = SarsaAgent(
|
||||
obs_n=env.observation_space.n,
|
||||
act_n=env.action_space.n,
|
||||
learning_rate=0.1,
|
||||
gamma=0.9,
|
||||
e_greed=0.1)
|
||||
|
||||
is_render = False
|
||||
for episode in range(500):
|
||||
ep_reward, ep_steps = run_episode(env, agent, is_render)
|
||||
print('Episode %s: steps = %s , reward = %.1f' % (episode, ep_steps,
|
||||
ep_reward))
|
||||
|
||||
# 每隔20个episode渲染一下看看效果（每个episode都渲染的话，时间会比较长）
|
||||
if episode % 20 == 0:
|
||||
is_render = True
|
||||
else:
|
||||
is_render = False
|
||||
# 训练结束，查看算法效果
|
||||
test_episode(env, agent)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user