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{
"cells": [
{
"cell_type": "markdown",
"id": "f68f85dd",
"metadata": {},
"source": [
"## outline & summary"
]
},
{
"cell_type": "markdown",
"id": "1a1c02db",
"metadata": {},
"source": [
"- q-learning => DQN（Deep Q Learning network)\n",
"- q_learning base q-table\n",
" - state 需要是/处理成离散的（discrete）\n",
" - q-table：行是state，列是action；\n",
" - $Q(s,a)$：动作价值（value），不是概率分布；\n",
" - $Q(s_t,a_t)$是在时刻 $t$，状态 $s_t$下采取动作 $a_t$ 时获得的折扣奖励总和（discounted total reward）\n",
" - 未来的价值折现到现在；\n",
" - image（pixels） as a state，状态变量的数量非常之大；\n",
"- dqn：nn(state) => action value，\n",
" - q table => q function（**拟合/回归**，state vector 与 action value 的关系） \n",
" - 输入输出：\n",
" - 输入（input）：state vector;\n",
" - cartpole：4d vector（位置，速度，角度，和角速度），甚至都需要care其semantic meaning；\n",
" - 输出：action space value，$Q(s_t,a_t)$\n",
" - shape：action space size；\n",
" - CartPole：2d（left/right）"
]
},
{
"cell_type": "markdown",
"id": "8d20ed05",
"metadata": {},
"source": [
"## DQN"
]
},
{
"cell_type": "markdown",
"id": "13072aae",
"metadata": {},
"source": [
"$$\n",
"\\begin{split}\n",
"&Q(s_t,a_t) = Q(s_t,a_t) + \\eta\\cdot (R_{t+1}+\\gamma \\max_a Q(s_{t+1},a)-Q(s_t,a_t))\\\\\n",
"&Q(s_t,a_t) = R_{t+1}+\\gamma\\cdot \\max_aQ(s_{t+1},a)\n",
"\\end{split}\n",
"$$"
]
},
{
"cell_type": "markdown",
"id": "4c7db45b",
"metadata": {},
"source": [
"- iteration algorithm\n",
"- temporal difference error（TD）: $R_{t+1}+\\gamma\\cdot \\max_aQ(s_t,a) - Q(s_t,a_t)$\n",
"- learning objective：\n",
" - MES（square loss）: $E(s_t,a_t)=\\left(R_{t+1}+\\gamma\\max_aQ(s_{t+1},a)-Q(s_t,a_t)\\right)^2$"
]
},
{
"cell_type": "markdown",
"id": "ed41247c",
"metadata": {},
"source": [
"### 几个核心技术"
]
},
{
"cell_type": "markdown",
"id": "aac3d784",
"metadata": {},
"source": [
"- experience replay（经验回放）\n",
" - 不像 q-table 的 q-learning，每一步都学习（update）该步的内容（experience）\n",
" - 对于 q-table 而言，每一步（step）都学习该步的内容，神经网络连续地学习时间上相关性高的内容（事实上，时间 $t$ 的学习内容，和时间 $t+1$ 的学习内容非常相似，这样的话，收敛就会很慢；\n",
" - 而是将每一步（step）的内容存储在经验池（experience pool）并随机从经验池中提取内容（replay，回放）让NN学习；\n",
" - 也是一种批次化（batch），使用经验池中的多个步骤的经验；"
]
},
{
"cell_type": "markdown",
"id": "aab5a212",
"metadata": {},
"source": [
"- loss function 使用 huber 而不是 square loss\n",
" - 误差很大时（$|\\delta|>1$），平方误差会导致误差函数的输出过大，导致学习难以稳定"
]
},
{
"cell_type": "markdown",
"id": "d7ed8605",
"metadata": {},
"source": [
"$$\n",
"\\mathcal L(\\delta)=\\begin{cases}\n",
"\\frac12\\delta^2, &\\quad |\\delta|\\leq 1,\\\\\n",
"|\\delta|-\\frac12, &\\quad |\\delta|\\gt 1\n",
"\\end{cases}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e7dbacd5",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T02:34:18.506052Z",
"start_time": "2023-03-19T02:34:18.503221Z"
}
},
"outputs": [],
"source": [
"from IPython.display import Image"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "babdb59c",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T02:36:40.839613Z",
"start_time": "2023-03-19T02:36:40.833258Z"
}
},
"outputs": [
{
"data": {
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 9,
"metadata": {
"image/png": {
"height": 450,
"width": 600
}
},
"output_type": "execute_result"
}
],
"source": [
"Image('./imgs/Huber_loss.svg.png', width=600, height=450)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "31d856d0",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T03:14:55.484348Z",
"start_time": "2023-03-19T03:14:55.252693Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x11bdf6340>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"def huber_loss(delta, beta=1):\n",
" if np.abs(delta) <= beta:\n",
" return 0.5*delta**2/beta\n",
" return abs(delta) - 0.5*beta\n",
"\n",
"def square_loss(delta):\n",
" return 0.5*delta**2\n",
"\n",
"deltas = np.arange(-5, 5, 0.01)\n",
"plt.plot(deltas, [huber_loss(delta) for delta in deltas])\n",
"plt.plot(deltas, square_loss(deltas))\n",
"plt.xticks(np.arange(-5, 5, step=1))\n",
"plt.legend(['huber loss', 'square loss'])"
]
},
{
"cell_type": "markdown",
"id": "5cb934f5",
"metadata": {},
"source": [
"## DQN 实现"
]
},
{
"cell_type": "markdown",
"id": "41ab9df6",
"metadata": {},
"source": [
"### experience replay"
]
},
{
"cell_type": "markdown",
"id": "f616ff16",
"metadata": {},
"source": [
"- 构造批次化训练数据\n",
"- 让整个训练过程更加稳定"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8af8286f",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T13:51:24.521834Z",
"start_time": "2023-03-19T13:51:24.513092Z"
}
},
"outputs": [],
"source": [
"from collections import namedtuple\n",
"import random"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "86b7c649",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T13:53:02.707627Z",
"start_time": "2023-03-19T13:53:02.704384Z"
}
},
"outputs": [],
"source": [
"# s_t, a_t => s_{t+1}\n",
"Transition = namedtuple('Transition', ('state', 'action', 'next_state', 'reward'))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "0bb63705",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T14:21:09.458289Z",
"start_time": "2023-03-19T14:21:09.452320Z"
}
},
"outputs": [],
"source": [
"class ReplayMemory:\n",
" \n",
" def __init__(self, capacity):\n",
" self.capacity = capacity\n",
" self.memory = []\n",
" self.index = 0\n",
" \n",
" def push(self, state, action, next_state, reward):\n",
" if len(self.memory) < self.capacity:\n",
" # placeholder\n",
" self.memory.append(None)\n",
" self.memory[self.index] = Transition(state, action, next_state, reward)\n",
" self.index = (self.index + 1) % self.capacity\n",
" \n",
" # list of transition\n",
" def sample(self, batch_size):\n",
" return random.sample(self.memory, batch_size)\n",
" def __len__(self):\n",
" return len(self.memory)"
]
},
{
"cell_type": "markdown",
"id": "b2a51a71",
"metadata": {},
"source": [
"### DQN network"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "6071a622",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T14:54:35.046524Z",
"start_time": "2023-03-19T14:54:35.043627Z"
}
},
"outputs": [],
"source": [
"from torch import nn\n",
"import torch\n",
"import torch.nn.functional as F\n",
"from torch import optim"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "b078d36e",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T14:55:27.636662Z",
"start_time": "2023-03-19T14:55:27.631846Z"
}
},
"outputs": [],
"source": [
"# Q_function base NN\n",
"class DQN(nn.Module):\n",
" def __init__(self, n_states, n_actions):\n",
" super(DQN, self).__init__()\n",
" self.fc1 = nn.Linear(n_states, 128)\n",
" self.fc2 = nn.Linear(128, 128)\n",
" self.fc3 = nn.Linear(128, n_actions)\n",
" def forward(self, x):\n",
" # x.shape: batch_size*n_states\n",
" # output.shape: batch_size*n_actions, state_action_value\n",
" return self.fc3(F.relu(self.fc2(F.relu(self.fc1(x)))))"
]
},
{
"cell_type": "markdown",
"id": "0931e93f",
"metadata": {},
"source": [
"### Agent"
]
},
{
"cell_type": "markdown",
"id": "d5e6ea7e",
"metadata": {},
"source": [
"- 计算过程中 tensor shape 的变化以及 shape的对齐"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "36484d7a",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T14:01:57.661322Z",
"start_time": "2023-03-19T14:01:57.655948Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Transition(state=(1, 5), action=(2, 6), next_state=(3, 7), reward=(4, 8))"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trs = [Transition(1, 2, 3, 4), Transition(5, 6, 7, 8)]\n",
"trs = Transition(*zip(*trs))\n",
"trs"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "7dd4ff4b",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T14:12:00.572027Z",
"start_time": "2023-03-19T14:12:00.564600Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[0.6664, 0.1629],\n",
" [0.9828, 0.9913],\n",
" [0.1747, 0.1865]])\n",
"tensor([0.6664, 0.9913, 0.1865])\n"
]
},
{
"data": {
"text/plain": [
"tensor([0, 1, 1])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t = torch.rand(3, 2)\n",
"print(t)\n",
"print(t.max(dim=1)[0])\n",
"t.max(dim=1)[1]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "af0d63d3",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T14:19:25.253604Z",
"start_time": "2023-03-19T14:19:25.249004Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 1])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.IntTensor([[random.randrange(2)]]).shape"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "60ef2edb",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T14:59:24.853559Z",
"start_time": "2023-03-19T14:59:24.840847Z"
}
},
"outputs": [],
"source": [
"class Agent:\n",
" def __init__(self, n_states, n_actions, eta=0.5, gamma=0.99, capacity=10000, batch_size=32):\n",
" self.n_states = n_states\n",
" self.n_actions = n_actions\n",
" self.eta = eta\n",
" self.gamma = gamma\n",
" self.batch_size = batch_size\n",
" \n",
" self.memory = ReplayMemory(capacity)\n",
" self.model = DQN(n_states, n_actions)\n",
" \n",
" self.optimizer = optim.Adam(self.model.parameters(), lr=0.0001)\n",
" \n",
" def _replay(self):\n",
" if len(self.memory) < self.batch_size:\n",
" return\n",
" # list of transition\n",
" batch = self.memory.sample(self.batch_size)\n",
" # Transition, column: len(tuple) == batch_size\n",
" batch = Transition(*zip(*batch))\n",
" \n",
" # s_t.shape: batch_size * 4\n",
" state_batch = torch.cat(batch.state)\n",
" # a_t.shape: batch_size * 1\n",
" action_batch = torch.cat(batch.action)\n",
" # r_{t+1}.shape: batch_size * 1\n",
" reward_batch = torch.cat(batch.reward)\n",
" # < batch_size\n",
" non_final_next_state_batch = torch.cat([s for s in batch.next_state if s is not None])\n",
" \n",
" \n",
" # 构造模型训练用的输入和输出（true）\n",
" # s_t, input\n",
" \n",
" # pred: Q(s_t, a_t)\n",
" # true: R_{t+1} + \\gamma*\\max_aQ(s_t, a)\n",
" \n",
" # 开启 eval 模式\n",
" self.model.eval()\n",
" \n",
" # pred, batch_size*1\n",
" state_action_values = self.model(state_batch).gather(dim=1, index=action_batch)\n",
" \n",
" # true: R_{t+1} + \\gamma*\\max_aQ(s_t, a)\n",
" # tuple(map(lambda s: s is not None, batch.next_state)): batch_size 长度的 0/1\n",
" non_final_mask = torch.ByteTensor(tuple(map(lambda s: s is not None, batch.next_state)))\n",
" next_state_values = torch.zeros(self.batch_size)\n",
" # Q(s_{t+1}, a)\n",
" next_state_values[non_final_mask] = self.model(non_final_next_state_batch).max(dim=1)[0].detach()\n",
" \n",
" # (batch_size, )\n",
" expected_state_action_values = reward_batch + self.gamma * next_state_values\n",
" \n",
" \n",
" # 开启train mode\n",
" self.model.train()\n",
" \n",
" # expected_state_action_values.unsqueeze(1): (batch_size, ) => (batch_size, 1)\n",
" loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1))\n",
" \n",
" self.optimizer.zero_grad()\n",
" loss.backward()\n",
" self.optimizer.step()\n",
" \n",
" def update_q_function(self):\n",
" self._replay()\n",
" \n",
" def memorize(self, state, action, next_state, reward):\n",
" self.memory.push(state, action, next_state, reward)\n",
" \n",
" # action policy\n",
" # epsilon_greedy\n",
" # double e: explore, exploit\n",
" def choose_action(self, state, episode):\n",
" eps = 0.5*1/(1+episode)\n",
" if random.random() < eps:\n",
" # explore\n",
" action = torch.IntTensor([[random.randrange(self.n_actions)]])\n",
" else:\n",
" self.model.eval()\n",
" with torch.no_grad():\n",
" action = self.model(state).max(1)[1].view(1, 1)\n",
" return action"
]
},
{
"cell_type": "markdown",
"id": "a8778abf",
"metadata": {},
"source": [
"### agent 与 env 进行交互"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "ddc0e17e",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T14:43:44.953180Z",
"start_time": "2023-03-19T14:43:44.918217Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'0.15.4'"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gym\n",
"gym.__version__"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "e3984cbe",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T14:44:13.985820Z",
"start_time": "2023-03-19T14:44:08.967408Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: http://mirrors.aliyun.com/pypi/simple/\r\n",
"Requirement already satisfied: pyglet==1.2.4 in /Users/chunhuizhang/opt/anaconda3/lib/python3.9/site-packages (1.2.4)\r\n"
]
}
],
"source": [
"!pip install pyglet==1.2.4"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "e06f0d0c",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T14:45:25.374607Z",
"start_time": "2023-03-19T14:45:25.347085Z"
}
},
"outputs": [],
"source": [
"env = gym.make('CartPole-v0')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "e3ce8297",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T14:46:18.887021Z",
"start_time": "2023-03-19T14:46:18.882935Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n",
"2\n"
]
}
],
"source": [
"print(env.observation_space.shape[0])\n",
"print(env.action_space.n)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "7aaa5a29",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T14:50:45.688240Z",
"start_time": "2023-03-19T14:50:45.683327Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([-0.03329124, -0.02317109, 0.00609648, -0.04172689])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"env.reset()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "2ccbb3a7",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T15:00:08.026818Z",
"start_time": "2023-03-19T15:00:07.214172Z"
}
},
"outputs": [],
"source": [
"from JSAnimation.IPython_display import display_animation\n",
"from IPython.display import display, HTML\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import animation"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "273ba101",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T15:00:17.320028Z",
"start_time": "2023-03-19T15:00:17.314275Z"
}
},
"outputs": [],
"source": [
"def display_frames_as_gif(frames, output):\n",
" \"\"\"\n",
" Displays a list of frames as a gif, with controls\n",
" 以gif格式显示关键帧列，带有控件\n",
" \"\"\"\n",
" \n",
" fig = plt.figure(figsize=(frames[0].shape[1]/72.0, frames[0].shape[0]/72.0),dpi=72)\n",
" patch = plt.imshow(frames[0])\n",
" plt.axis('off')\n",
" \n",
" def animate(i):\n",
" img = patch.set_data(frames[i])\n",
" return img ## *** return是必须要有的 ***\n",
" \n",
" anim = animation.FuncAnimation(plt.gcf(), animate, frames=len(frames), interval=50)\n",
" \n",
" anim.save(output)\n",
" return HTML(anim.to_jshtml()) ## *** 返回一个HTML对象，以便被调用者显示。 ***\n",
" # display(display_animation(anim, default_mode='loop'))"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "9ab95edc",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T15:01:36.702583Z",
"start_time": "2023-03-19T15:01:25.974325Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"episode: 0, steps: 11\n",
"episode: 1, steps: 10\n",
"episode: 2, steps: 10\n",
"episode: 3, steps: 8\n",
"episode: 4, steps: 11\n",
"episode: 5, steps: 8\n",
"episode: 6, steps: 8\n",
"episode: 7, steps: 12\n",
"episode: 8, steps: 15\n",
"episode: 9, steps: 11\n",
"episode: 10, steps: 8\n",
"episode: 11, steps: 9\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/1j/fv7y5kz53592pt9xx4tqfrl80000gn/T/ipykernel_29531/153055639.py:49: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. (Triggered internally at /Users/runner/work/_temp/anaconda/conda-bld/pytorch_1670525473998/work/aten/src/ATen/native/IndexingUtils.h:28.)\n",
" next_state_values[non_final_mask] = self.model(non_final_next_state_batch).max(dim=1)[0].detach()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"episode: 12, steps: 10\n",
"episode: 13, steps: 9\n",
"episode: 14, steps: 15\n",
"episode: 15, steps: 34\n",
"episode: 16, steps: 16\n",
"episode: 17, steps: 19\n",
"episode: 18, steps: 29\n",
"episode: 19, steps: 17\n",
"episode: 20, steps: 12\n",
"episode: 21, steps: 13\n",
"episode: 22, steps: 33\n",
"episode: 23, steps: 24\n",
"episode: 24, steps: 51\n",
"episode: 25, steps: 98\n",
"episode: 26, steps: 64\n",
"episode: 27, steps: 57\n",
"episode: 28, steps: 128\n",
"episode: 29, steps: 93\n",
"episode: 30, steps: 83\n",
"episode: 31, steps: 119\n",
"episode: 32, steps: 136\n",
"episode: 33, steps: 199\n",
"episode: 34, steps: 199\n",
"episode: 35, steps: 121\n",
"episode: 36, steps: 199\n",
"episode: 37, steps: 199\n",
"episode: 38, steps: 199\n",
"episode: 39, steps: 199\n",
"episode: 40, steps: 199\n",
"episode: 41, steps: 199\n",
"episode: 42, steps: 199\n",
"episode: 43, steps: 199\n",
"episode: 44, steps: 199\n",
"episode: 45, steps: 199\n",
"连续成功10轮\n",
"episode: 46, steps: 199\n"
]
}
],
"source": [
"env = gym.make('CartPole-v0')\n",
"n_states = env.observation_space.shape[0]\n",
"n_actions = env.action_space.n\n",
"\n",
"max_episodes = 500\n",
"max_steps = 200\n",
"\n",
"complete_episodes = 0\n",
"finished_flag = False\n",
"\n",
"agent = Agent(n_states, n_actions)\n",
"frames = []\n",
"\n",
"for episode in range(max_episodes):\n",
" state = env.reset()\n",
" state = torch.from_numpy(state).type(torch.FloatTensor).unsqueeze(0)\n",
" for step in range(max_steps):\n",
" if finished_flag == True:\n",
" frames.append(env.render(mode='rgb_array'))\n",
" # IntTensor of 1*1\n",
" action = agent.choose_action(state, episode)\n",
" \n",
" # transition on env\n",
" next_state, _, done, _ = env.step(action.item())\n",
" \n",
" if done:\n",
" next_state = None\n",
" \n",
" if step < 195:\n",
" # 1d\n",
" reward = torch.FloatTensor([-1.])\n",
" complete_episodes = 0\n",
" else:\n",
" reward = torch.FloatTensor([1.])\n",
" complete_episodes += 1\n",
" else:\n",
" reward = torch.FloatTensor([0])\n",
" # (4, )\n",
" next_state = torch.from_numpy(next_state).type(torch.FloatTensor)\n",
" # (4, ) ==> (1, 4)，便于后续的 torch.cat => (1, 4) => (32, 4)\n",
" next_state = next_state.unsqueeze(0)\n",
" agent.memorize(state, action, next_state, reward)\n",
" agent.update_q_function()\n",
" state = next_state\n",
" \n",
" if done:\n",
" print(f'episode: {episode}, steps: {step}')\n",
" break\n",
" if finished_flag == True:\n",
" break\n",
" \n",
" if complete_episodes >= 10:\n",
" finished_flag = True\n",
" print('连续成功10轮')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "3aad8611",
"metadata": {
"ExecuteTime": {
"end_time": "2023-03-19T15:02:20.097047Z",
"start_time": "2023-03-19T15:02:01.538094Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<link rel=\"stylesheet\"\n",
"href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
"<script language=\"javascript\">\n",
" function isInternetExplorer() {\n",
" ua = navigator.userAgent;\n",
" /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
" return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
" }\n",
"\n",
" /* Define the Animation class */\n",
" function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
" this.img_id = img_id;\n",
" this.slider_id = slider_id;\n",
" this.loop_select_id = loop_select_id;\n",
" this.interval = interval;\n",
" this.current_frame = 0;\n",
" this.direction = 0;\n",
" this.timer = null;\n",
" this.frames = new Array(frames.length);\n",
"\n",
" for (var i=0; i<frames.length; i++)\n",
" {\n",
" this.frames[i] = new Image();\n",
" this.frames[i].src = frames[i];\n",
" }\n",
" var slider = document.getElementById(this.slider_id);\n",
" slider.max = this.frames.length - 1;\n",
" if (isInternetExplorer()) {\n",
" // switch from oninput to onchange because IE <= 11 does not conform\n",
" // with W3C specification. It ignores oninput and onchange behaves\n",
" // like oninput. In contrast, Microsoft Edge behaves correctly.\n",
" slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
" slider.setAttribute('oninput', null);\n",
" }\n",
" this.set_frame(this.current_frame);\n",
" }\n",
"\n",
" Animation.prototype.get_loop_state = function(){\n",
" var button_group = document[this.loop_select_id].state;\n",
" for (var i = 0; i < button_group.length; i++) {\n",
" var button = button_group[i];\n",
" if (button.checked) {\n",
" return button.value;\n",
" }\n",
" }\n",
" return undefined;\n",
" }\n",
"\n",
" Animation.prototype.set_frame = function(frame){\n",
" this.current_frame = frame;\n",
" document.getElementById(this.img_id).src =\n",
" this.frames[this.current_frame].src;\n",
" document.getElementById(this.slider_id).value = this.current_frame;\n",
" }\n",
"\n",
" Animation.prototype.next_frame = function()\n",
" {\n",
" this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
" }\n",
"\n",
" Animation.prototype.previous_frame = function()\n",
" {\n",
" this.set_frame(Math.max(0, this.current_frame - 1));\n",
" }\n",
"\n",
" Animation.prototype.first_frame = function()\n",
" {\n",
" this.set_frame(0);\n",
" }\n",
"\n",
" Animation.prototype.last_frame = function()\n",
" {\n",
" this.set_frame(this.frames.length - 1);\n",
" }\n",
"\n",
" Animation.prototype.slower = function()\n",
" {\n",
" this.interval /= 0.7;\n",
" if(this.direction > 0){this.play_animation();}\n",
" else if(this.direction < 0){this.reverse_animation();}\n",
" }\n",
"\n",
" Animation.prototype.faster = function()\n",
" {\n",
" this.interval *= 0.7;\n",
" if(this.direction > 0){this.play_animation();}\n",
" else if(this.direction < 0){this.reverse_animation();}\n",
" }\n",
"\n",
" Animation.prototype.anim_step_forward = function()\n",
" {\n",
" this.current_frame += 1;\n",
" if(this.current_frame < this.frames.length){\n",
" this.set_frame(this.current_frame);\n",
" }else{\n",
" var loop_state = this.get_loop_state();\n",
" if(loop_state == \"loop\"){\n",
" this.first_frame();\n",
" }else if(loop_state == \"reflect\"){\n",
" this.last_frame();\n",
" this.reverse_animation();\n",
" }else{\n",
" this.pause_animation();\n",
" this.last_frame();\n",
" }\n",
" }\n",
" }\n",
"\n",
" Animation.prototype.anim_step_reverse = function()\n",
" {\n",
" this.current_frame -= 1;\n",
" if(this.current_frame >= 0){\n",
" this.set_frame(this.current_frame);\n",
" }else{\n",
" var loop_state = this.get_loop_state();\n",
" if(loop_state == \"loop\"){\n",
" this.last_frame();\n",
" }else if(loop_state == \"reflect\"){\n",
" this.first_frame();\n",
" this.play_animation();\n",
" }else{\n",
" this.pause_animation();\n",
" this.first_frame();\n",
" }\n",
" }\n",
" }\n",
"\n",
" Animation.prototype.pause_animation = function()\n",
" {\n",
" this.direction = 0;\n",
" if (this.timer){\n",
" clearInterval(this.timer);\n",
" this.timer = null;\n",
" }\n",
" }\n",
"\n",
" Animation.prototype.play_animation = function()\n",
" {\n",
" this.pause_animation();\n",
" this.direction = 1;\n",
" var t = this;\n",
" if (!this.timer) this.timer = setInterval(function() {\n",
" t.anim_step_forward();\n",
" }, this.interval);\n",
" }\n",
"\n",
" Animation.prototype.reverse_animation = function()\n",
" {\n",
" this.pause_animation();\n",
" this.direction = -1;\n",
" var t = this;\n",
" if (!this.timer) this.timer = setInterval(function() {\n",
" t.anim_step_reverse();\n",
" }, this.interval);\n",
" }\n",
"</script>\n",
"\n",
"<style>\n",
".animation {\n",
" display: inline-block;\n",
" text-align: center;\n",
"}\n",
"input[type=range].anim-slider {\n",
" width: 374px;\n",
" margin-left: auto;\n",
" margin-right: auto;\n",
"}\n",
".anim-buttons {\n",
" margin: 8px 0px;\n",
"}\n",
".anim-buttons button {\n",
" padding: 0;\n",
" width: 36px;\n",
"}\n",
".anim-state label {\n",
" margin-right: 8px;\n",
"}\n",
".anim-state input {\n",
" margin: 0;\n",
" vertical-align: middle;\n",
"}\n",
"</style>\n",
"\n",
"<div class=\"animation\">\n",
" <img id=\"_anim_img6a4258095ee24d0caef87292711334d8\">\n",
" <div class=\"anim-controls\">\n",
" <input id=\"_anim_slider6a4258095ee24d0caef87292711334d8\" type=\"range\" class=\"anim-slider\"\n",
" name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
" oninput=\"anim6a4258095ee24d0caef87292711334d8.set_frame(parseInt(this.value));\">\n",
" <div class=\"anim-buttons\">\n",
" <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"anim6a4258095ee24d0caef87292711334d8.slower()\">\n",
" <i class=\"fa fa-minus\"></i></button>\n",
" <button title=\"First frame\" aria-label=\"First frame\" onclick=\"anim6a4258095ee24d0caef87292711334d8.first_frame()\">\n",
" <i class=\"fa fa-fast-backward\"></i></button>\n",
" <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"anim6a4258095ee24d0caef87292711334d8.previous_frame()\">\n",
" <i class=\"fa fa-step-backward\"></i></button>\n",
" <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"anim6a4258095ee24d0caef87292711334d8.reverse_animation()\">\n",
" <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
" <button title=\"Pause\" aria-label=\"Pause\" onclick=\"anim6a4258095ee24d0caef87292711334d8.pause_animation()\">\n",
" <i class=\"fa fa-pause\"></i></button>\n",
" <button title=\"Play\" aria-label=\"Play\" onclick=\"anim6a4258095ee24d0caef87292711334d8.play_animation()\">\n",
" <i class=\"fa fa-play\"></i></button>\n",
" <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"anim6a4258095ee24d0caef87292711334d8.next_frame()\">\n",
" <i class=\"fa fa-step-forward\"></i></button>\n",
" <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"anim6a4258095ee24d0caef87292711334d8.last_frame()\">\n",
" <i class=\"fa fa-fast-forward\"></i></button>\n",
" <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"anim6a4258095ee24d0caef87292711334d8.faster()\">\n",
" <i class=\"fa fa-plus\"></i></button>\n",
" </div>\n",
" <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select6a4258095ee24d0caef87292711334d8\"\n",
" class=\"anim-state\">\n",
" <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_6a4258095ee24d0caef87292711334d8\"\n",
" >\n",
" <label for=\"_anim_radio1_6a4258095ee24d0caef87292711334d8\">Once</label>\n",
" <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_6a4258095ee24d0caef87292711334d8\"\n",
" checked>\n",
" <label for=\"_anim_radio2_6a4258095ee24d0caef87292711334d8\">Loop</label>\n",
" <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_6a4258095ee24d0caef87292711334d8\"\n",
" >\n",
" <label for=\"_anim_radio3_6a4258095ee24d0caef87292711334d8\">Reflect</label>\n",
" </form>\n",
" </div>\n",
"</div>\n",
"\n",
"\n",
"<script language=\"javascript\">\n",
" /* Instantiate the Animation class. */\n",
" /* The IDs given should match those used in the template above. */\n",
" (function() {\n",
" var img_id = \"_anim_img6a4258095ee24d0caef87292711334d8\";\n",
" var slider_id = \"_anim_slider6a4258095ee24d0caef87292711334d8\";\n",
" var loop_select_id = \"_anim_loop_select6a4258095ee24d0caef87292711334d8\";\n",
" var frames = new Array(200);\n",
" \n",
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"text/plain": [
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]
},
"metadata": {},
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}
],
"source": [
"display_frames_as_gif(frames, 'save/dqn_cartpole.gif')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7ee31133",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"language": "python",
"name": "python3"
},
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},
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"name": "python",
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"version": "3.9.13"
},
"toc": {
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"title_cell": "Table of Contents",
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"toc_position": {
"height": "calc(100% - 180px)",
"left": "10px",
"top": "150px",
"width": "336px"
},
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}
},
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}
|
