DDPG-v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "oGidxBPYFuVF"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import random\n",
    "from  torch.autograd import Variable\n",
    "import os \n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "import copy\n",
    "from collections import namedtuple, deque\n",
    "import gym\n",
    "import time as time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of each action: 2\n",
      "Each observes a state with length: {} 8\n",
      "The state for the first agent looks like: -0.00046176912\n",
      "Threshold:  200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\gym\\core.py:172: DeprecationWarning: \u001b[33mWARN: Function `env.seed(seed)` is marked as deprecated and will be removed in the future. Please use `env.reset(seed=seed) instead.\u001b[0m\n",
      "  deprecation(\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "BUFFER_SIZE = int(1e6)\n",
    "BATCH_SIZE = 64\n",
    "GAMMA = 0.99\n",
    "TAU = 1e-3\n",
    "LR_ACTOR = 1e-4\n",
    "LR_CRITIC = 1e-3\n",
    "WEIGHT_DECAY = 0.01\n",
    "EPSILON = 0\n",
    "EPSILON_DECAY = 1e-6\n",
    "LEARNING_PERIOD = 20\n",
    "UPDATE_FACTOR   = 10\n",
    "\n",
    "env = gym.make('LunarLanderContinuous-v2')\n",
    "seed = 101\n",
    "env.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "np.random.seed(seed)\n",
    "\n",
    "state = env.reset()\n",
    "state_dim = env.observation_space.shape[0]\n",
    "action_dim = env.action_space.shape[0]\n",
    "max_action = float(env.action_space.high[0])\n",
    "threshold = env.spec.reward_threshold\n",
    "\n",
    "print('Size of each action:', action_dim)\n",
    "print('Each observes a state with length: {}', state_dim)\n",
    "print('The state for the first agent looks like:', state[0])\n",
    "print('Threshold: ', threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DDPG Actor and Critic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "pN6tHMx9kvcZ"
   },
   "outputs": [],
   "source": [
    "def hidden_init(layer):\n",
    "    limit = 1. / (layer.weight.data.size()[0] **0.5)\n",
    "    return (-limit, limit)\n",
    "\n",
    "class Actor(nn.Module):\n",
    "    def __init__(self, state_size, action_size, seed, fc1_units=64, fc2_units=128):\n",
    "        super(Actor, self).__init__()\n",
    "        self.seed = torch.manual_seed(seed)\n",
    "        self.network = nn.Sequential(\n",
    "            nn.Linear(state_size, fc1_units),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(fc1_units, fc2_units),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(fc2_units, action_size),\n",
    "        )\n",
    "        self.param_reset()\n",
    "\n",
    "    def param_reset(self):\n",
    "        self.network[0].weight.data.uniform_(*hidden_init(self.network[0]))\n",
    "        self.network[2].weight.data.uniform_(*hidden_init(self.network[2]))\n",
    "        self.network[4].weight.data.uniform_(-0.003, 0.003)\n",
    "\n",
    "    def forward(self, state):\n",
    "        x = self.network[:4](state)\n",
    "        x = F.relu(self.network[1](x))\n",
    "        x = F.relu(self.network[3](x))\n",
    "        return torch.tanh(self.network[4](x))\n",
    "    \n",
    "class Critic(nn.Module):\n",
    "    def __init__(self, state_size, action_size, seed, fcs1_units=128, fc2_units=128):\n",
    "        super(Critic, self).__init__()\n",
    "        self.seed = torch.manual_seed(seed)\n",
    "        self.fcs1 = nn.Sequential(\n",
    "            nn.Linear(state_size, fcs1_units),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "        self.fc2 = nn.Sequential(\n",
    "            nn.Linear(fcs1_units + action_size, fc2_units),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "        self.fc3 = nn.Linear(fc2_units, 1)\n",
    "        self.param_reset()\n",
    "\n",
    "    def param_reset(self):\n",
    "        self.fcs1[0].weight.data.uniform_(*hidden_init(self.fcs1[0]))\n",
    "        self.fc2[0].weight.data.uniform_(*hidden_init(self.fc2[0]))\n",
    "        self.fc3.weight.data.uniform_(-3e-3, 3e-3)\n",
    "\n",
    "    def forward(self, state, action):\n",
    "        xs = self.fcs1(state)\n",
    "        x = torch.cat((xs, action), dim=1)\n",
    "        x = self.fc2(x)\n",
    "        return self.fc3(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "6pngYDOJkyq6"
   },
   "outputs": [],
   "source": [
    "class Agent():\n",
    "\n",
    "    def __init__(self, state_size, action_size, random_seed):\n",
    "        self.state_size = state_size\n",
    "        self.action_size = action_size\n",
    "        self.seed = random.seed(random_seed)\n",
    "        self.epsilon = EPSILON\n",
    "\n",
    "        self.actor_local = Actor(state_size, action_size, random_seed).to(device)\n",
    "        self.actor_target = Actor(state_size, action_size, random_seed).to(device)\n",
    "        self.actor_optimizer = optim.Adam(self.actor_local.parameters(), lr=LR_ACTOR)\n",
    "\n",
    "        self.critic_local = Critic(state_size, action_size, random_seed).to(device)\n",
    "        self.critic_target = Critic(state_size, action_size, random_seed).to(device)\n",
    "        self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=LR_CRITIC, weight_decay=WEIGHT_DECAY)\n",
    "\n",
    "        self.noise = OUNoise(action_size, random_seed)\n",
    "\n",
    "        self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)\n",
    "\n",
    "        # Make sure target is with the same weight as the source\n",
    "        self.hard_update(self.actor_target, self.actor_local)\n",
    "        self.hard_update(self.critic_target, self.critic_local)\n",
    "\n",
    "    def step(self, state, action, reward, next_state, done, timestep):   \n",
    "        self.memory.add(state, action, reward, next_state, done)\n",
    "\n",
    "        if len(self.memory) > BATCH_SIZE and timestep % LEARNING_PERIOD == 0:\n",
    "            for _ in range(UPDATE_FACTOR):\n",
    "                experiences = self.memory.sample()\n",
    "                self.learn(experiences, GAMMA)\n",
    "\n",
    "    def act(self, state, add_noise=True):\n",
    "        state = torch.from_numpy(state).float().to(device)\n",
    "\n",
    "        self.actor_local.eval()\n",
    "        with torch.no_grad():\n",
    "            action = self.actor_local(state).cpu().data.numpy()\n",
    "        self.actor_local.train()\n",
    "\n",
    "        if add_noise:\n",
    "            action += self.epsilon * self.noise.sample()\n",
    "\n",
    "        return np.clip(action, -1, 1)\n",
    "\n",
    "    def reset(self):\n",
    "        self.noise.reset()\n",
    "\n",
    "    def learn(self, experiences, gamma):\n",
    "\n",
    "        states, actions, rewards, next_states, dones = experiences\n",
    "\n",
    "        actions_next = self.actor_target(next_states)\n",
    "        Q_targets_next = self.critic_target(next_states, actions_next)\n",
    "\n",
    "        Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))\n",
    "\n",
    "        Q_expected = self.critic_local(states, actions)\n",
    "        critic_loss = F.mse_loss(Q_expected, Q_targets)\n",
    "\n",
    "        self.critic_optimizer.zero_grad()\n",
    "        critic_loss.backward()\n",
    "        self.critic_optimizer.step()\n",
    "\n",
    "        actions_pred = self.actor_local(states)\n",
    "        actor_loss = -self.critic_local(states, actions_pred).mean()\n",
    "\n",
    "        self.actor_optimizer.zero_grad()\n",
    "        actor_loss.backward()\n",
    "        self.actor_optimizer.step()\n",
    "\n",
    "        self.soft_update(self.critic_local, self.critic_target, TAU)\n",
    "        self.soft_update(self.actor_local, self.actor_target, TAU)\n",
    "\n",
    "        self.epsilon -= EPSILON_DECAY\n",
    "        self.noise.reset()\n",
    "\n",
    "    def soft_update(self, local_model, target_model, tau):\n",
    "        for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):\n",
    "            target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)\n",
    "\n",
    "    def hard_update(self, target, source):\n",
    "        for target_param, param in zip(target.parameters(), source.parameters()):\n",
    "            target_param.data.copy_(param.data)\n",
    "\n",
    "        \n",
    "class OUNoise:\n",
    "    def __init__(self, size, seed, mu=0., theta=0.15, sigma=0.2):\n",
    "        self.mu = mu * np.ones(size)\n",
    "        self.theta = theta\n",
    "        self.sigma = sigma\n",
    "        self.seed = random.seed(seed)\n",
    "        self.reset()\n",
    "\n",
    "    def reset(self):\n",
    "        self.state = copy.copy(self.mu)\n",
    "\n",
    "    def sample(self):\n",
    "        x = self.state\n",
    "        dx = self.theta * (self.mu - x) + self.sigma * np.array([random.random() for i in range(len(x))])\n",
    "        self.state = x + dx\n",
    "        return self.state\n",
    "    \n",
    "class ReplayBuffer:\n",
    "\n",
    "    def __init__(self, action_size, buffer_size, batch_size, seed):\n",
    "\n",
    "        self.action_size = action_size\n",
    "        self.memory = deque(maxlen=buffer_size)\n",
    "        self.batch_size = batch_size\n",
    "        self.experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n",
    "        self.seed = random.seed(seed)\n",
    "\n",
    "    def add(self, state, action, reward, next_state, done):\n",
    "        e = self.experience(state, action, reward, next_state, done)\n",
    "        self.memory.append(e)\n",
    "\n",
    "    def sample(self):\n",
    "        experiences = random.sample(self.memory, k=self.batch_size)\n",
    "\n",
    "        states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)\n",
    "        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).float().to(device)\n",
    "        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)\n",
    "        next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)\n",
    "        dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)\n",
    "\n",
    "        return (states, actions, rewards, next_states, dones)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pFk9d0s6kyvo",
    "outputId": "69aa7885-da4e-475a-cb2c-4e18a1b9ae7f",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 10 Score: -278.80 Average Score: -317.85, Time: 00:00:03 ***\n",
      "Episode 20 Score: -384.45 Average Score: -514.60, Time: 00:00:07 ***\n",
      "Episode 30 Score: -996.82 Average Score: -593.64, Time: 00:00:12 ***\n",
      "Episode 40 Score: -2239.78 Average Score: -902.29, Time: 00:00:22 ***\n",
      "Episode 50 Score: -2747.44 Average Score: -1278.45, Time: 00:00:33 ***\n",
      "Episode 60 Score: -2558.40 Average Score: -1495.70, Time: 00:00:43 ***\n",
      "Episode 70 Score: -285.38 Average Score: -1431.67, Time: 00:00:50 ***\n",
      "Episode 80 Score: -277.52 Average Score: -1283.07, Time: 00:00:53 ***\n",
      "Episode 90 Score: -174.57 Average Score: -1168.37, Time: 00:01:09 ***\n",
      "Episode 100 Score: -1567.28 Average Score: -1062.75, Time: 00:01:33 ***\n",
      "Episode 110 Score: -27.13 Average Score: -1044.58, Time: 00:01:58 ***\n",
      "Episode 120 Score: -83.68 Average Score: -981.76, Time: 00:02:20 ***\n",
      "Episode 130 Score: 33.87 Average Score: -908.14, Time: 00:02:47 ***\n",
      "Episode 140 Score: -42.48 Average Score: -725.60, Time: 00:02:56 ***\n",
      "Episode 150 Score: -58.51 Average Score: -447.07, Time: 00:03:19 ***\n",
      "Episode 160 Score: 212.29 Average Score: -177.00, Time: 00:03:34 ***\n",
      "Episode 170 Score: 228.72 Average Score: -53.22, Time: 00:03:52 ***\n",
      "Episode 180 Score: 191.63 Average Score: -11.84, Time: 00:04:10 ***\n",
      "Episode 190 Score: 191.78 Average Score: 33.58, Time: 00:04:28 ***\n",
      "Episode 200 Score: -1.15 Average Score: 58.73, Time: 00:04:51 ***\n",
      "Episode 210 Score: 241.83 Average Score: 85.06, Time: 00:04:59 ***\n",
      "Episode 220 Score: 241.81 Average Score: 110.56, Time: 00:05:10 ***\n",
      "Episode 230 Score: 237.98 Average Score: 124.09, Time: 00:05:22 ***\n",
      "Episode 240 Score: -28.75 Average Score: 138.46, Time: 00:05:33 ***\n",
      "Episode 250 Score: -9.08 Average Score: 150.60, Time: 00:05:42 ***\n",
      "Episode 260 Score: 249.02 Average Score: 159.25, Time: 00:05:53 ***\n",
      "Episode 270 Score: 195.29 Average Score: 154.95, Time: 00:06:02 ***\n",
      "Episode 280 Score: 277.12 Average Score: 152.39, Time: 00:06:10 ***\n",
      "Episode 290 Score: 262.04 Average Score: 152.26, Time: 00:06:19 ***\n",
      "Episode 300 Score: 214.85 Average Score: 158.14, Time: 00:06:29 ***\n",
      "Episode 310 Score: 262.82 Average Score: 165.64, Time: 00:06:38 ***\n",
      "Episode 320 Score: 256.58 Average Score: 168.72, Time: 00:06:48 ***\n",
      "Episode 330 Score: -39.17 Average Score: 167.21, Time: 00:06:57 ***\n",
      "Episode 340 Score: 192.34 Average Score: 169.88, Time: 00:07:09 ***\n",
      "Episode 350 Score: -64.71 Average Score: 172.52, Time: 00:07:21 ***\n",
      "Episode 360 Score: -33.79 Average Score: 162.99, Time: 00:07:33 ***\n",
      "Episode 370 Score: 197.68 Average Score: 144.42, Time: 00:07:49 ***\n",
      "Episode 380 Score: -137.29 Average Score: 124.90, Time: 00:08:01 ***\n",
      "Episode 390 Score: -176.75 Average Score: 108.32, Time: 00:08:21 ***\n",
      "Episode 400 Score: -222.93 Average Score: 77.15, Time: 00:08:33 ***\n",
      "Episode 410 Score: 177.01 Average Score: 56.56, Time: 00:08:50 ***\n",
      "Episode 420 Score: 170.22 Average Score: 38.59, Time: 00:09:06 ***\n",
      "Episode 430 Score: 207.45 Average Score: 34.91, Time: 00:09:28 ***\n",
      "Episode 440 Score: 203.81 Average Score: 22.65, Time: 00:09:50 ***\n",
      "Episode 450 Score: 113.35 Average Score: 14.01, Time: 00:10:14 ***\n",
      "Episode 460 Score: -150.42 Average Score: 9.96, Time: 00:10:37 ***\n",
      "Episode 470 Score: -56.44 Average Score: 13.63, Time: 00:10:59 ***\n",
      "Episode 480 Score: -203.67 Average Score: 27.43, Time: 00:11:19 ***\n",
      "Episode 490 Score: 158.83 Average Score: 28.86, Time: 00:11:34 ***\n",
      "Episode 500 Score: 147.61 Average Score: 47.93, Time: 00:11:52 ***\n",
      "Episode 510 Score: -101.12 Average Score: 48.37, Time: 00:12:06 ***\n",
      "Episode 520 Score: -127.80 Average Score: 38.28, Time: 00:12:20 ***\n",
      "Episode 530 Score: -156.65 Average Score: 33.70, Time: 00:12:42 ***\n",
      "Episode 540 Score: -152.88 Average Score: 27.42, Time: 00:13:00 ***\n",
      "Episode 550 Score: -123.54 Average Score: 13.66, Time: 00:13:18 ***\n",
      "Episode 560 Score: 125.94 Average Score: 1.98, Time: 00:13:38 ***\n",
      "Episode 570 Score: -54.37 Average Score: -2.19, Time: 00:13:57 ***\n",
      "Episode 580 Score: -121.45 Average Score: -10.95, Time: 00:14:23 ***\n",
      "Episode 590 Score: -113.88 Average Score: -15.42, Time: 00:14:52 ***\n",
      "Episode 600 Score: -103.28 Average Score: -31.45, Time: 00:15:03 ***\n",
      "Episode 610 Score: -145.86 Average Score: -35.68, Time: 00:15:22 ***\n",
      "Episode 620 Score: -113.61 Average Score: -40.41, Time: 00:15:33 ***\n",
      "Episode 630 Score: -67.38 Average Score: -49.82, Time: 00:15:53 ***\n",
      "Episode 640 Score: 70.92 Average Score: -52.62, Time: 00:16:20 ***\n",
      "Episode 650 Score: -80.99 Average Score: -52.61, Time: 00:16:45 ***\n",
      "Episode 660 Score: -204.63 Average Score: -55.96, Time: 00:17:17 ***\n",
      "Episode 670 Score: 119.67 Average Score: -56.83, Time: 00:17:50 ***\n",
      "Episode 680 Score: -164.90 Average Score: -55.44, Time: 00:18:19 ***\n",
      "Episode 690 Score: 249.16 Average Score: -53.45, Time: 00:18:50 ***\n",
      "Episode 700 Score: 124.56 Average Score: -43.82, Time: 00:19:25 ***\n",
      "Episode 710 Score: 276.89 Average Score: -30.79, Time: 00:19:54 ***\n",
      "Episode 720 Score: 246.66 Average Score: -7.20, Time: 00:20:23 ***\n",
      "Episode 730 Score: 2.02 Average Score: 8.40, Time: 00:20:52 ***\n",
      "Episode 740 Score: 4.08 Average Score: 24.85, Time: 00:21:19 ***\n",
      "Episode 750 Score: 299.81 Average Score: 49.87, Time: 00:21:40 ***\n",
      "Episode 760 Score: 102.41 Average Score: 70.64, Time: 00:22:08 ***\n",
      "Episode 770 Score: 259.96 Average Score: 89.95, Time: 00:22:36 ***\n",
      "Episode 780 Score: 101.76 Average Score: 99.93, Time: 00:23:08 ***\n",
      "Episode 790 Score: 215.02 Average Score: 114.87, Time: 00:23:35 ***\n",
      "Episode 800 Score: 4.14 Average Score: 127.74, Time: 00:24:05 ***\n",
      "Episode 810 Score: 156.05 Average Score: 135.70, Time: 00:24:28 ***\n",
      "Episode 820 Score: 19.88 Average Score: 142.76, Time: 00:24:53 ***\n",
      "Episode 830 Score: 145.65 Average Score: 150.10, Time: 00:25:20 ***\n",
      "Episode 840 Score: 19.56 Average Score: 154.42, Time: 00:25:45 ***\n",
      "Episode 850 Score: 250.80 Average Score: 154.01, Time: 00:26:11 ***\n",
      "Episode 860 Score: 197.44 Average Score: 151.72, Time: 00:26:47 ***\n",
      "Episode 870 Score: 189.49 Average Score: 155.19, Time: 00:27:05 ***\n",
      "Episode 880 Score: 214.13 Average Score: 164.87, Time: 00:27:21 ***\n",
      "Episode 890 Score: 298.76 Average Score: 162.44, Time: 00:27:42 ***\n",
      "Episode 900 Score: -32.40 Average Score: 166.57, Time: 00:28:01 ***\n",
      "Episode 910 Score: 273.99 Average Score: 174.25, Time: 00:28:15 ***\n",
      "Episode 920 Score: 50.36 Average Score: 176.87, Time: 00:28:36 ***\n",
      "Episode 930 Score: -6.51 Average Score: 169.47, Time: 00:29:05 ***\n",
      "Episode 940 Score: -15.80 Average Score: 169.05, Time: 00:29:32 ***\n",
      "Episode 950 Score: 245.96 Average Score: 177.86, Time: 00:29:42 ***\n",
      "Episode 960 Score: 226.27 Average Score: 182.13, Time: 00:30:09 ***\n",
      "Episode 970 Score: 264.95 Average Score: 189.11, Time: 00:30:18 ***\n",
      "Episode 980 Score: 215.19 Average Score: 187.33, Time: 00:30:27 ***\n",
      "Episode 990 Score: 266.78 Average Score: 198.60, Time: 00:30:38 ***\n",
      "Episode 997 Score: 220.60 Average Score: 200.20, Time: 00:30:46 ***\n",
      "Episode 1000 Score: 172.71 Average Score: 200.49, Time: 00:30:49 ***\n",
      "Episode 1001 Score: 289.41 Average Score: 200.63, Time: 00:30:50 ***\n",
      "Episode 1010 Score: 303.15 Average Score: 199.43, Time: 00:31:00 ***\n",
      "Episode 1020 Score: 156.60 Average Score: 200.48, Time: 00:31:16 ***\n",
      "Episode 1021 Score: 233.13 Average Score: 200.66, Time: 00:31:17 ***\n",
      "Episode 1022 Score: 237.33 Average Score: 201.91, Time: 00:31:18 ***\n",
      "Episode 1023 Score: 261.40 Average Score: 202.76, Time: 00:31:18 ***\n",
      "Episode 1024 Score: 256.18 Average Score: 202.88, Time: 00:31:19 ***\n",
      "Episode 1025 Score: 276.28 Average Score: 205.79, Time: 00:31:20 ***\n",
      "Episode 1026 Score: 204.45 Average Score: 206.47, Time: 00:31:20 ***\n",
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      "Episode 1080 Score: 283.52 Average Score: 233.89, Time: 00:32:06 ***\n",
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      "Episode 1641 Score: 272.95 Average Score: 244.37, Time: 00:40:28 ***\n",
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      "Episode 1811 Score: 293.87 Average Score: 245.21, Time: 00:42:54 ***\n",
      "Episode 1812 Score: 190.65 Average Score: 244.53, Time: 00:42:55 ***\n",
      "Episode 1813 Score: 279.91 Average Score: 247.25, Time: 00:42:55 ***\n",
      "Episode 1814 Score: 219.75 Average Score: 247.06, Time: 00:42:56 ***\n",
      "Episode 1815 Score: 257.13 Average Score: 247.14, Time: 00:42:57 ***\n",
      "Episode 1816 Score: 287.92 Average Score: 247.50, Time: 00:42:57 ***\n",
      "Episode 1817 Score: 242.44 Average Score: 248.33, Time: 00:42:59 ***\n",
      "Episode 1818 Score: 293.17 Average Score: 248.60, Time: 00:43:00 ***\n",
      "Episode 1819 Score: 266.21 Average Score: 248.31, Time: 00:43:01 ***\n",
      "Episode 1820 Score: 248.25 Average Score: 248.15, Time: 00:43:02 ***\n",
      "Episode 1821 Score: 106.91 Average Score: 246.19, Time: 00:43:07 ***\n",
      "Episode 1822 Score: 272.15 Average Score: 248.35, Time: 00:43:08 ***\n",
      "Episode 1823 Score: 249.34 Average Score: 248.24, Time: 00:43:09 ***\n",
      "Episode 1824 Score: 11.40 Average Score: 245.82, Time: 00:43:09 ***\n",
      "Episode 1825 Score: 268.73 Average Score: 245.94, Time: 00:43:10 ***\n",
      "Episode 1826 Score: 215.88 Average Score: 245.57, Time: 00:43:11 ***\n",
      "Episode 1827 Score: 293.11 Average Score: 245.80, Time: 00:43:12 ***\n",
      "Episode 1828 Score: 32.08 Average Score: 243.27, Time: 00:43:12 ***\n",
      "Episode 1829 Score: 246.56 Average Score: 242.97, Time: 00:43:13 ***\n",
      "Episode 1830 Score: 48.57 Average Score: 240.85, Time: 00:43:14 ***\n",
      "Episode 1831 Score: 241.18 Average Score: 240.31, Time: 00:43:14 ***\n",
      "Episode 1832 Score: 36.59 Average Score: 237.64, Time: 00:43:15 ***\n",
      "Episode 1833 Score: 230.91 Average Score: 237.34, Time: 00:43:16 ***\n",
      "Episode 1834 Score: 286.03 Average Score: 237.39, Time: 00:43:18 ***\n",
      "Episode 1835 Score: 233.67 Average Score: 237.00, Time: 00:43:19 ***\n",
      "Episode 1836 Score: 305.83 Average Score: 237.35, Time: 00:43:19 ***\n",
      "Episode 1837 Score: 288.80 Average Score: 237.52, Time: 00:43:22 ***\n",
      "Episode 1838 Score: 266.79 Average Score: 237.40, Time: 00:43:23 ***\n",
      "Episode 1839 Score: 296.75 Average Score: 237.57, Time: 00:43:24 ***\n",
      "Episode 1840 Score: 246.46 Average Score: 237.22, Time: 00:43:25 ***\n",
      "Episode 1841 Score: 248.98 Average Score: 236.75, Time: 00:43:25 ***\n",
      "Episode 1842 Score: 270.39 Average Score: 237.00, Time: 00:43:26 ***\n",
      "Episode 1843 Score: 294.91 Average Score: 237.03, Time: 00:43:27 ***\n",
      "Episode 1844 Score: 43.09 Average Score: 234.70, Time: 00:43:27 ***\n",
      "Episode 1845 Score: 235.31 Average Score: 236.27, Time: 00:43:28 ***\n",
      "Episode 1846 Score: 221.90 Average Score: 236.63, Time: 00:43:29 ***\n",
      "Episode 1847 Score: 262.14 Average Score: 236.71, Time: 00:43:30 ***\n",
      "Episode 1848 Score: 267.83 Average Score: 236.94, Time: 00:43:31 ***\n",
      "Episode 1849 Score: 232.42 Average Score: 236.57, Time: 00:43:32 ***\n",
      "Episode 1850 Score: 274.67 Average Score: 236.66, Time: 00:43:33 ***\n",
      "Episode 1851 Score: 321.90 Average Score: 237.05, Time: 00:43:33 ***\n",
      "Episode 1852 Score: 216.56 Average Score: 239.00, Time: 00:43:35 ***\n",
      "Episode 1853 Score: 232.11 Average Score: 238.65, Time: 00:43:36 ***\n",
      "Episode 1854 Score: 258.56 Average Score: 239.04, Time: 00:43:36 ***\n",
      "Episode 1855 Score: 217.52 Average Score: 238.35, Time: 00:43:39 ***\n",
      "Episode 1856 Score: 302.94 Average Score: 238.85, Time: 00:43:40 ***\n",
      "Episode 1857 Score: 290.64 Average Score: 239.06, Time: 00:43:41 ***\n",
      "Episode 1858 Score: 272.63 Average Score: 239.03, Time: 00:43:41 ***\n",
      "Episode 1859 Score: 297.78 Average Score: 241.94, Time: 00:43:42 ***\n",
      "Episode 1860 Score: 264.05 Average Score: 241.85, Time: 00:43:43 ***\n",
      "Episode 1861 Score: 259.18 Average Score: 241.94, Time: 00:43:43 ***\n",
      "Episode 1862 Score: 257.45 Average Score: 241.98, Time: 00:43:44 ***\n",
      "Episode 1863 Score: 267.39 Average Score: 241.69, Time: 00:43:45 ***\n",
      "Episode 1864 Score: 248.27 Average Score: 241.88, Time: 00:43:45 ***\n",
      "Episode 1865 Score: 269.55 Average Score: 242.13, Time: 00:43:46 ***\n",
      "Episode 1866 Score: 253.53 Average Score: 241.92, Time: 00:43:47 ***\n",
      "Episode 1867 Score: 281.34 Average Score: 242.06, Time: 00:43:48 ***\n",
      "Episode 1868 Score: 260.64 Average Score: 242.21, Time: 00:43:49 ***\n",
      "Episode 1869 Score: 282.01 Average Score: 242.56, Time: 00:43:49 ***\n",
      "Episode 1870 Score: 256.48 Average Score: 242.44, Time: 00:43:50 ***\n",
      "Episode 1871 Score: 264.78 Average Score: 242.20, Time: 00:43:51 ***\n",
      "Episode 1872 Score: 5.30 Average Score: 239.15, Time: 00:43:52 ***\n",
      "Episode 1873 Score: 305.16 Average Score: 243.50, Time: 00:43:53 ***\n",
      "Episode 1874 Score: 269.16 Average Score: 243.27, Time: 00:43:53 ***\n",
      "Episode 1875 Score: 272.94 Average Score: 243.17, Time: 00:43:55 ***\n",
      "Episode 1876 Score: 252.74 Average Score: 245.30, Time: 00:43:56 ***\n",
      "Episode 1877 Score: 276.74 Average Score: 245.22, Time: 00:43:57 ***\n",
      "Episode 1878 Score: 284.92 Average Score: 245.73, Time: 00:43:57 ***\n",
      "Episode 1879 Score: 255.76 Average Score: 245.43, Time: 00:44:00 ***\n",
      "Episode 1880 Score: 260.12 Average Score: 245.49, Time: 00:44:01 ***\n",
      "Episode 1881 Score: 50.83 Average Score: 243.19, Time: 00:44:01 ***\n",
      "Episode 1882 Score: 308.00 Average Score: 243.66, Time: 00:44:02 ***\n",
      "Episode 1883 Score: 277.32 Average Score: 243.77, Time: 00:44:03 ***\n",
      "Episode 1884 Score: 283.55 Average Score: 243.67, Time: 00:44:04 ***\n",
      "Episode 1885 Score: 32.17 Average Score: 241.23, Time: 00:44:04 ***\n",
      "Episode 1886 Score: 241.53 Average Score: 241.21, Time: 00:44:05 ***\n",
      "Episode 1887 Score: 293.48 Average Score: 241.77, Time: 00:44:06 ***\n",
      "Episode 1888 Score: 261.80 Average Score: 241.54, Time: 00:44:08 ***\n",
      "Episode 1889 Score: 27.98 Average Score: 239.56, Time: 00:44:08 ***\n",
      "Episode 1890 Score: 268.48 Average Score: 239.53, Time: 00:44:10 ***\n",
      "Episode 1891 Score: 41.92 Average Score: 237.43, Time: 00:44:10 ***\n",
      "Episode 1892 Score: 245.62 Average Score: 237.09, Time: 00:44:11 ***\n",
      "Episode 1893 Score: 243.91 Average Score: 237.14, Time: 00:44:12 ***\n",
      "Episode 1894 Score: 238.49 Average Score: 237.17, Time: 00:44:13 ***\n",
      "Episode 1895 Score: 279.06 Average Score: 237.08, Time: 00:44:13 ***\n",
      "Episode 1896 Score: 270.98 Average Score: 237.14, Time: 00:44:14 ***\n",
      "Episode 1897 Score: 272.80 Average Score: 237.74, Time: 00:44:15 ***\n",
      "Episode 1898 Score: 236.81 Average Score: 237.46, Time: 00:44:16 ***\n",
      "Episode 1899 Score: 274.79 Average Score: 238.53, Time: 00:44:17 ***\n",
      "Episode 1900 Score: 251.61 Average Score: 238.48, Time: 00:44:17 ***\n",
      "Episode 1901 Score: 250.08 Average Score: 237.75, Time: 00:44:19 ***\n",
      "Episode 1902 Score: 288.90 Average Score: 238.29, Time: 00:44:19 ***\n",
      "Episode 1903 Score: 274.79 Average Score: 238.17, Time: 00:44:20 ***\n",
      "Episode 1904 Score: 261.67 Average Score: 238.21, Time: 00:44:21 ***\n",
      "Episode 1905 Score: 242.71 Average Score: 238.02, Time: 00:44:21 ***\n",
      "Episode 1906 Score: 283.87 Average Score: 238.44, Time: 00:44:22 ***\n",
      "Episode 1907 Score: 244.54 Average Score: 239.11, Time: 00:44:23 ***\n",
      "Episode 1908 Score: 296.57 Average Score: 239.52, Time: 00:44:24 ***\n",
      "Episode 1909 Score: 265.52 Average Score: 239.36, Time: 00:44:25 ***\n",
      "Episode 1910 Score: 274.48 Average Score: 239.51, Time: 00:44:26 ***\n",
      "Episode 1911 Score: 246.61 Average Score: 239.04, Time: 00:44:27 ***\n",
      "Episode 1912 Score: 254.69 Average Score: 239.68, Time: 00:44:29 ***\n",
      "Episode 1913 Score: 243.58 Average Score: 239.32, Time: 00:44:30 ***\n",
      "Episode 1914 Score: 272.14 Average Score: 239.84, Time: 00:44:30 ***\n",
      "Episode 1915 Score: 242.83 Average Score: 239.70, Time: 00:44:31 ***\n",
      "Episode 1916 Score: 47.54 Average Score: 237.29, Time: 00:44:32 ***\n",
      "Episode 1917 Score: 288.04 Average Score: 237.75, Time: 00:44:32 ***\n",
      "Episode 1918 Score: 269.80 Average Score: 237.51, Time: 00:44:33 ***\n",
      "Episode 1919 Score: 241.89 Average Score: 237.27, Time: 00:44:35 ***\n",
      "Episode 1920 Score: 281.03 Average Score: 237.60, Time: 00:44:35 ***\n",
      "Episode 1921 Score: 286.43 Average Score: 239.39, Time: 00:44:36 ***\n",
      "Episode 1922 Score: 223.95 Average Score: 238.91, Time: 00:44:37 ***\n",
      "Episode 1923 Score: 229.93 Average Score: 238.72, Time: 00:44:37 ***\n",
      "Episode 1924 Score: -108.20 Average Score: 237.52, Time: 00:44:38 ***\n",
      "Episode 1925 Score: -68.23 Average Score: 234.15, Time: 00:44:40 ***\n",
      "Episode 1926 Score: 264.00 Average Score: 234.63, Time: 00:44:41 ***\n",
      "Episode 1927 Score: 280.53 Average Score: 234.51, Time: 00:44:42 ***\n",
      "Episode 1928 Score: 264.97 Average Score: 236.84, Time: 00:44:42 ***\n",
      "Episode 1929 Score: 287.37 Average Score: 237.25, Time: 00:44:43 ***\n",
      "Episode 1930 Score: 273.06 Average Score: 239.49, Time: 00:44:44 ***\n",
      "Episode 1931 Score: 258.10 Average Score: 239.66, Time: 00:44:45 ***\n",
      "Episode 1932 Score: 27.32 Average Score: 239.57, Time: 00:44:46 ***\n",
      "Episode 1933 Score: 197.77 Average Score: 239.23, Time: 00:44:46 ***\n",
      "Episode 1934 Score: 297.53 Average Score: 239.35, Time: 00:44:47 ***\n",
      "Episode 1935 Score: 265.80 Average Score: 239.67, Time: 00:44:48 ***\n",
      "Episode 1936 Score: 280.20 Average Score: 239.42, Time: 00:44:49 ***\n",
      "Episode 1937 Score: 246.58 Average Score: 238.99, Time: 00:44:49 ***\n",
      "Episode 1938 Score: 275.31 Average Score: 239.08, Time: 00:44:50 ***\n",
      "Episode 1939 Score: 283.90 Average Score: 238.95, Time: 00:44:51 ***\n",
      "Episode 1940 Score: 274.67 Average Score: 239.23, Time: 00:44:51 ***\n",
      "Episode 1941 Score: 256.92 Average Score: 239.31, Time: 00:44:52 ***\n",
      "Episode 1942 Score: 257.56 Average Score: 239.18, Time: 00:44:53 ***\n",
      "Episode 1943 Score: 240.85 Average Score: 238.64, Time: 00:44:53 ***\n",
      "Episode 1944 Score: 233.17 Average Score: 240.54, Time: 00:44:54 ***\n",
      "Episode 1945 Score: 278.07 Average Score: 240.97, Time: 00:44:55 ***\n",
      "Episode 1946 Score: 268.35 Average Score: 241.43, Time: 00:44:55 ***\n",
      "Episode 1947 Score: 249.91 Average Score: 241.31, Time: 00:44:56 ***\n",
      "Episode 1948 Score: 138.88 Average Score: 240.02, Time: 00:44:58 ***\n",
      "Episode 1949 Score: 279.52 Average Score: 240.49, Time: 00:44:59 ***\n",
      "Episode 1950 Score: 293.06 Average Score: 240.68, Time: 00:45:00 ***\n",
      "Episode 1951 Score: 280.51 Average Score: 240.26, Time: 00:45:00 ***\n",
      "Episode 1952 Score: 276.12 Average Score: 240.86, Time: 00:45:01 ***\n",
      "Episode 1953 Score: 252.71 Average Score: 241.07, Time: 00:45:02 ***\n",
      "Episode 1954 Score: 277.07 Average Score: 241.25, Time: 00:45:03 ***\n",
      "Episode 1955 Score: 272.57 Average Score: 241.80, Time: 00:45:04 ***\n",
      "Episode 1956 Score: 244.81 Average Score: 241.22, Time: 00:45:04 ***\n",
      "Episode 1957 Score: 275.46 Average Score: 241.07, Time: 00:45:05 ***\n",
      "Episode 1958 Score: 270.62 Average Score: 241.05, Time: 00:45:06 ***\n",
      "Episode 1959 Score: 264.54 Average Score: 240.72, Time: 00:45:07 ***\n",
      "Episode 1960 Score: 238.61 Average Score: 240.46, Time: 00:45:08 ***\n",
      "Episode 1961 Score: 288.63 Average Score: 240.76, Time: 00:45:08 ***\n",
      "Episode 1962 Score: 249.29 Average Score: 240.67, Time: 00:45:09 ***\n",
      "Episode 1963 Score: 279.96 Average Score: 240.80, Time: 00:45:10 ***\n",
      "Episode 1964 Score: 257.84 Average Score: 240.90, Time: 00:45:11 ***\n",
      "Episode 1965 Score: 292.78 Average Score: 241.13, Time: 00:45:13 ***\n",
      "Episode 1966 Score: 254.49 Average Score: 241.14, Time: 00:45:13 ***\n",
      "Episode 1967 Score: 58.72 Average Score: 238.91, Time: 00:45:14 ***\n",
      "Episode 1968 Score: 288.52 Average Score: 239.19, Time: 00:45:14 ***\n",
      "Episode 1969 Score: 276.98 Average Score: 239.14, Time: 00:45:15 ***\n",
      "Episode 1970 Score: 40.28 Average Score: 236.98, Time: 00:45:16 ***\n",
      "Episode 1971 Score: 245.38 Average Score: 236.78, Time: 00:45:16 ***\n",
      "Episode 1972 Score: 56.21 Average Score: 237.29, Time: 00:45:17 ***\n",
      "Episode 1973 Score: 290.41 Average Score: 237.15, Time: 00:45:17 ***\n",
      "Episode 1974 Score: 289.08 Average Score: 237.35, Time: 00:45:18 ***\n",
      "Episode 1975 Score: 212.17 Average Score: 236.74, Time: 00:45:19 ***\n",
      "Episode 1976 Score: 240.95 Average Score: 236.62, Time: 00:45:19 ***\n",
      "Episode 1977 Score: 248.55 Average Score: 236.34, Time: 00:45:20 ***\n",
      "Episode 1978 Score: 275.84 Average Score: 236.25, Time: 00:45:21 ***\n",
      "Episode 1979 Score: 270.34 Average Score: 236.39, Time: 00:45:21 ***\n",
      "Episode 1980 Score: 248.51 Average Score: 236.28, Time: 00:45:22 ***\n",
      "Episode 1981 Score: 259.66 Average Score: 238.36, Time: 00:45:23 ***\n",
      "Episode 1982 Score: 266.06 Average Score: 237.95, Time: 00:45:24 ***\n",
      "Episode 1983 Score: 240.19 Average Score: 237.57, Time: 00:45:25 ***\n",
      "Episode 1984 Score: 255.19 Average Score: 237.29, Time: 00:45:26 ***\n",
      "Episode 1985 Score: 260.92 Average Score: 239.58, Time: 00:45:27 ***\n",
      "Episode 1986 Score: 291.28 Average Score: 240.08, Time: 00:45:28 ***\n",
      "Episode 1987 Score: 256.70 Average Score: 239.71, Time: 00:45:29 ***\n",
      "Episode 1988 Score: 294.78 Average Score: 240.04, Time: 00:45:30 ***\n",
      "Episode 1989 Score: 239.45 Average Score: 242.15, Time: 00:45:31 ***\n",
      "Episode 1990 Score: 279.19 Average Score: 242.26, Time: 00:45:32 ***\n",
      "Episode 1991 Score: 273.28 Average Score: 244.57, Time: 00:45:35 ***\n",
      "Episode 1992 Score: 257.87 Average Score: 244.70, Time: 00:45:36 ***\n",
      "Episode 1993 Score: 274.52 Average Score: 245.00, Time: 00:45:37 ***\n",
      "Episode 1994 Score: 268.94 Average Score: 245.31, Time: 00:45:37 ***\n",
      "Episode 1995 Score: 301.24 Average Score: 245.53, Time: 00:45:38 ***\n",
      "Episode 1996 Score: 265.79 Average Score: 245.48, Time: 00:45:39 ***\n",
      "Episode 1997 Score: 280.91 Average Score: 245.56, Time: 00:45:40 ***\n",
      "Episode 1998 Score: 26.84 Average Score: 243.46, Time: 00:45:40 ***\n",
      "Episode 1999 Score: 267.02 Average Score: 243.38, Time: 00:45:41 ***\n",
      "Episode 2000 Score: 275.69 Average Score: 243.62, Time: 00:45:42 ***\n"
     ]
    }
   ],
   "source": [
    "agent = Agent(state_size=state_dim, action_size=action_dim, random_seed=8)\n",
    "\n",
    "def ddpg(n_episodes=2000, print_every=10):\n",
    "    scores_deque = deque(maxlen=100)\n",
    "    scores_array = []\n",
    "    avg_scores_array = []\n",
    "\n",
    "    time_start = time.time()\n",
    "\n",
    "    timestep_after_last_save = 0\n",
    "    total_timesteps = 0\n",
    "\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "        state = env.reset()\n",
    "        agent.reset()\n",
    "        score_average = 0\n",
    "\n",
    "        timestep = 0\n",
    "        total_reward = 0\n",
    "\n",
    "        done = False\n",
    "\n",
    "        while True:\n",
    "            action = agent.act(state)\n",
    "\n",
    "            # Perform action\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            done_bool = 0 if timestep + 1 == env._max_episode_steps else float(done)\n",
    "            total_reward += reward\n",
    "\n",
    "            agent.step(state, action, reward, next_state, done, timestep)\n",
    "            state = next_state\n",
    "\n",
    "            timestep += 1\n",
    "            total_timesteps += 1\n",
    "            timestep_after_last_save += 1\n",
    "\n",
    "            if done:\n",
    "                break\n",
    "\n",
    "        scores_deque.append(total_reward)\n",
    "        scores_array.append(total_reward)\n",
    "\n",
    "        avg_score = np.mean(scores_deque)\n",
    "        avg_scores_array.append(avg_score)\n",
    "\n",
    "        s = (int)(time.time() - time_start)\n",
    "\n",
    "        if i_episode % print_every == 0 or (len(scores_deque) == 100 and np.mean(scores_deque) >= threshold):\n",
    "            s = (int)(time.time() - time_start)\n",
    "            print('Episode {} Score: {:.2f} Average Score: {:.2f}, Time: {:02}:{:02}:{:02} ***'\\\n",
    "                  .format(i_episode, total_reward, avg_score, s//3600, s%3600//60, s%60))\n",
    "\n",
    "\n",
    "    return scores_array, avg_scores_array\n",
    "\n",
    "scores, avg_scores = ddpg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "wwyPu_oolZvl"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "ax.plot(scores, alpha=0.8, label='Total Rewards per Episode')\n",
    "ax.plot(avg_scores, label='Rolling Average')\n",
    "\n",
    "ax.set_xlabel('Episode')\n",
    "ax.set_ylabel('Reward')\n",
    "ax.set_title('Rewards and Moving Average Rewards per Episode')\n",
    "ax.legend()\n",
    "ax.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save(agent, filename, directory):\n",
    "    os.makedirs(directory, exist_ok=True)\n",
    "    \n",
    "    checkpoint = {\n",
    "        'actor_local_state_dict': agent.actor_local.state_dict(),\n",
    "        'actor_target_state_dict': agent.actor_target.state_dict(),\n",
    "        'critic_local_state_dict': agent.critic_local.state_dict(),\n",
    "        'critic_target_state_dict': agent.critic_target.state_dict(),\n",
    "\n",
    "        'actor_optimizer_state_dict': agent.actor_optimizer.state_dict(),\n",
    "        'critic_optimizer_state_dict': agent.critic_optimizer.state_dict()\n",
    "    }\n",
    "    \n",
    "    torch.save(checkpoint, os.path.join(directory, f'{filename}.pth'))\n",
    "\n",
    "def load(agent, directory, filename):\n",
    "    filepath = f'{directory}' + f'{filename}.pth'\n",
    "    checkpoint = torch.load(filepath)\n",
    "    \n",
    "    agent.actor_local.load_state_dict(checkpoint['actor_local_state_dict'])\n",
    "    agent.actor_target.load_state_dict(checkpoint['actor_target_state_dict'])\n",
    "    agent.critic_local.load_state_dict(checkpoint['critic_local_state_dict'])\n",
    "    agent.critic_target.load_state_dict(checkpoint['critic_target_state_dict'])\n",
    "    \n",
    "    # Optionally load the optimizer state\n",
    "    agent.actor_optimizer.load_state_dict(checkpoint['actor_optimizer_state_dict'])\n",
    "    agent.critic_optimizer.load_state_dict(checkpoint['critic_optimizer_state_dict'])\n",
    "\n",
    "# save(agent, 'LunarLander_DDPG', './Saved_files/DDPG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trained_agent = load(agent, './Saved_files/DDPG/', 'LunarLander_DDPG',) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "XPlZDgXvlZxT"
   },
   "outputs": [],
   "source": [
    "def play(env, agent, n_episodes):\n",
    "\n",
    "    state = env.reset()\n",
    "    scores_deque = deque(maxlen=100)\n",
    "\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "        state = env.reset()\n",
    "        agent.reset()\n",
    "\n",
    "        total_reward = 0\n",
    "        time_start = time.time()\n",
    "        timesteps =  0\n",
    "\n",
    "        done = False\n",
    "\n",
    "        while True:\n",
    "\n",
    "            action = agent.act(state)\n",
    "            env.render()\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            total_reward += reward\n",
    "\n",
    "            agent.step(state, action, reward, next_state, done, timesteps)\n",
    "            state = next_state\n",
    "            timesteps += 1\n",
    "\n",
    "            if done:\n",
    "                break\n",
    "\n",
    "        delta = (int)(time.time() - time_start)\n",
    "\n",
    "        scores_deque.append(total_reward)\n",
    "\n",
    "        print('Episode {}\\tAverage Score: {:.2f}, \\t Timesteps: {} \\tTime: {:02}:{:02}:{:02}'\\\n",
    "                  .format(i_episode, np.mean(scores_deque), timesteps,\\\n",
    "                          delta//3600, delta%3600//60, delta%60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "V9NPTF6LlfZo"
   },
   "outputs": [],
   "source": [
    "play(env=env, agent=agent, n_episodes=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ua_nY2SXllw2"
   },
   "outputs": [],
   "source": [
    "load(agent, './Saved_files/DDPG', '/LunarLander_DDPG')\n",
    "play(env=env, agent=agent, n_episodes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = np.array(scores)\n",
    "scores.dump(open('./Saved_files/DDPG/DDPG_scores.npy', 'wb'))\n",
    "\n",
    "avg_scores = np.array(avg_scores)\n",
    "avg_scores.dump(open('./Saved_files/DDPG/DDPG_avg_scores.npy', 'wb'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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