DQN-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tensor Manipulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import random\n",
    "import cv2\n",
    "import gym\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage import color\n",
    "import skimage.transform as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[(1, 5), (2, 6), (3, 7), (4, 8)]\nTransition(state=(1, 5), action=(2, 6), next_state=(3, 7), reward=(4, 8))\n"
    }
   ],
   "source": [
    "Transition = namedtuple('Transition', \n",
    "                        ('state', 'action', 'next_state', 'reward'))\n",
    "transitions = [Transition(1,2,3,4),Transition(5,6,7,8)]\n",
    "print(list(zip(*transitions)))\n",
    "a=Transition(*zip(*transitions))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Transition(state=1, action=2, next_state=3, reward=4)\n"
    }
   ],
   "source": [
    "print(Transition(*[1,2,3,4]))\n",
    "#print(Transition([1,2,3,4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReplayMemory():\n",
    "    def __init__(self, capacity):\n",
    "        self.capacity = capacity\n",
    "        self.memory = []\n",
    "        self.position = 0\n",
    "    \n",
    "    def push(self, *args):\n",
    "        \"\"\"save as a transition\"\"\"\n",
    "        if len(self.memory) < self.capacity:\n",
    "            self.memory.append(None)\n",
    "\n",
    "        self.memory[self.position] = Transition(*args)\n",
    "        self.position = (self.position + 1) % self.capacity\n",
    "\n",
    "    def sample(self, batch_size):\n",
    "        return random.sample(self.memory, batch_size)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(tensor([[57]]), tensor([[51]]), tensor([[97]]))\ntensor([[57],\n        [51],\n        [97]])\n"
    }
   ],
   "source": [
    "memory = ReplayMemory(100)\n",
    "for i in range(100):\n",
    "    memory.push(torch.tensor([[i]]),torch.tensor([[i]]),torch.tensor([[i]]),torch.tensor([[i]]))\n",
    "transitions = memory.sample(3)\n",
    "batch = Transition(*zip(*transitions))\n",
    "#print(batch)\n",
    "state_batch = torch.cat(batch.state)\n",
    "print(batch.state)\n",
    "print(state_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[Transition(state=tensor([[99]]), action=tensor([[99]]), next_state=None, reward=tensor([[99]]))]\n"
    }
   ],
   "source": [
    "memory = ReplayMemory(100)\n",
    "memory.push(torch.tensor([[i]]),torch.tensor([[i]]), None, torch.tensor([[i]]))\n",
    "print(memory.memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(tensor([[57]]), tensor([[51]]), tensor([[97]]))\ntensor([[57],\n        [51],\n        [97]])\n"
    }
   ],
   "source": [
    "print(batch.next_state)\n",
    "non_terminal = torch.cat([s for s in batch.next_state if s is not None])\n",
    "print(non_terminal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "tensor([True, True, True])\n"
    }
   ],
   "source": [
    "non_terminal_mask = torch.tensor(tuple(map(lambda s: s.item() is not 12,\n",
    "                                        batch.next_state)), device='cpu', \n",
    "                                        dtype=torch.bool)\n",
    "print(non_terminal_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "tensor([[57],\n        [51],\n        [97]])"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "non_terminal[non_terminal_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# c = torch.zeros((3,1))\n",
    "# c[[False, True, True]] = b\n",
    "# print(c)\n",
    "# c = torch.zeros((3,1))\n",
    "# c[non_terminal_mask] = b    #to show non_termial_mask is a list of bool numbers\n",
    "# print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "tensor([[[-1.7181, -0.6962],\n         [ 0.2353, -1.2120]],\n\n        [[ 0.5682,  1.1439],\n         [ 0.1395,  1.2377]]])\ntensor([[0.5682, 1.1439],\n        [0.2353, 1.2377]])\n"
    }
   ],
   "source": [
    "a = torch.randn(1,2,2)\n",
    "b = torch.randn(1,2,2)\n",
    "c = torch.cat((a,b))\n",
    "print(c)\n",
    "d, _ = c.max(dim=0)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "torch.float32 0.0 False\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x1430ec6d8>"
     },
     "metadata": {},
     "execution_count": 12
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "env = gym.make('SpaceInvaders-v0')\n",
    "obs = env.reset()\n",
    "device = 'cpu'\n",
    "num_frames = 4\n",
    "total_reward = 0\n",
    "state = torch.zeros((num_frames, 84, 84))\n",
    "obs_buffer = np.zeros((2, ) + env.observation_space.shape, dtype=np.uint8)\n",
    "action = env.action_space.sample()\n",
    "obs, reward, done, info = env.step(action)\n",
    "obs_buffer[0] = obs\n",
    "for i in range(num_frames):\n",
    "    obs, reward, done, info = env.step(action)\n",
    "    obs_buffer[1] = obs\n",
    "    total_reward += reward\n",
    "    max_frame = transforms.resize(obs_buffer.max(axis=0), (84, 84))\n",
    "    state[i] = torch.from_numpy(color.rgb2gray(max_frame)).float().to(device) #(84, 84)\n",
    "    if done:\n",
    "        env.reset()\n",
    "        break\n",
    "    obs_buffer[0] = obs\n",
    "print(state.unsqueeze(0).dtype, total_reward, done)\n",
    "plt.imshow(state.permute(1,2,0)[:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x14341d2b0>"
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.imshow(state.permute(1,2,0)[:,:,:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CartPole DQN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "torch.Size([1, 4])\n"
    }
   ],
   "source": [
    "env = gym.make('CartPole-v0')\n",
    "obs = env.reset()\n",
    "print(torch.from_numpy(obs).unsqueeze(0).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "torch.Size([10, 2])\n"
    }
   ],
   "source": [
    "class FcDQN(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(FcDQN, self).__init__()\n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "\n",
    "        self.fc_net = nn.Sequential(\n",
    "            nn.Linear(self.input_dim, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, self.output_dim)\n",
    "        )\n",
    "\n",
    "    def forward(self, observation):\n",
    "        qvals = self.fc_net(observation.view(-1, self.input_dim))\n",
    "        return qvals\n",
    "model = FcDQN(4,2)\n",
    "x = torch.ones((10,4))\n",
    "y = model(x)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Actor(nn.Module):\n",
    "    def __init__(self, state_dim, action_dim, action_bounds, offset):\n",
    "        super(Actor, self).__init__()\n",
    "        # actor\n",
    "        self.actor = nn.Sequential(\n",
    "                        nn.Linear(state_dim + state_dim, 64),\n",
    "                        nn.ReLU(),\n",
    "                        nn.Linear(64, 64),\n",
    "                        nn.ReLU(),\n",
    "                        nn.Linear(64, action_dim),\n",
    "                        nn.Tanh()\n",
    "                        )\n",
    "        # max value of actions\n",
    "        self.action_bounds = action_bounds\n",
    "        self.offset = offset\n",
    "        \n",
    "    def forward(self, state, goal):\n",
    "        return (self.actor(torch.cat([state, goal], 1)) * self.action_bounds) + self.offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(4,)\n"
    }
   ],
   "source": [
    "actor = Actor(2,2,1,0)\n",
    "a = actor(torch.ones(2,2), torch.ones(2,2)).detach().data.numpy().flatten()\n",
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvDQN(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(ConvDQN, self).__init__()\n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        #if feature_dim = 6272, input_batch / 2 = output_batch\n",
    "        self.feature_dim = 3136\n",
    "\n",
    "        self.conv_net = nn.Sequential(\n",
    "            nn.Conv2d(self.input_dim, 32, kernel_size=8, stride=4),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(32, 64, kernel_size=4, stride=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(64, 64, kernel_size=3, stride=1),\n",
    "            nn.ReLU()\n",
    "            )\n",
    "\n",
    "        self.fc_net = nn.Sequential(\n",
    "            nn.Linear(self.feature_dim, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, self.output_dim)\n",
    "            )\n",
    "\n",
    "    def forward(self, observation):\n",
    "        features = self.conv_net(observation)\n",
    "        qvals = self.fc_net(features.view(-1, self.feature_dim))\n",
    "        return qvals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "torch.Size([18, 10])\n"
    }
   ],
   "source": [
    "model = ConvDQN(3, 10)\n",
    "state = torch.ones(18, 3, 84, 84)\n",
    "output = model(state)\n",
    "print(output.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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