Continuous_Control.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import random\n",
    "from collections import namedtuple, deque\n",
    "import time\n",
    "import copy\n",
    "\n",
    "from shutil import copyfile\n",
    "from IPython.display import display, clear_output\n",
    "from unityagents import UnityEnvironment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hidden_init(layer):\n",
    "    fanin = layer.weight.data.size()[0]\n",
    "    limit = 1. / np.sqrt(fanin)\n",
    "    return (-limit, limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Actor(nn.Module):\n",
    "\n",
    "    def __init__(self, state_size, action_size, seed, fc1_units=256, fc2_units=128):\n",
    "        \"\"\" \n",
    "        PARAMETERS: \n",
    "            state_size (int): Dimension of a state\n",
    "            action_size (int): Dimension of an action\n",
    "            seed (int): Rand seed\n",
    "            fc1_units (int): No. of nodes in 1st hidden layer\n",
    "            fc2_units (int): No. of nodes in 2nd hidden layer\n",
    "        \"\"\"\n",
    "        super(Actor, self).__init__()\n",
    "        self.seed = torch.manual_seed(seed)\n",
    "        self.fc1 = nn.Linear(state_size, fc1_units)\n",
    "        self.fc2 = nn.Linear(fc1_units, fc2_units)\n",
    "        self.fc3 = nn.Linear(fc2_units, action_size)\n",
    "        self.reset_parameters()\n",
    "    def reset_parameters(self):\n",
    "        self.fc1.weight.data.uniform_(*hidden_init(self.fc1))\n",
    "        self.fc2.weight.data.uniform_(*hidden_init(self.fc2))\n",
    "        self.fc3.weight.data.uniform_(-3e-3 , 3e-3)\n",
    "    def forward(self , state):\n",
    "        inp = F.relu(self.fc1(state))\n",
    "        inp = F.relu(self.fc2(inp))\n",
    "        return torch.tanh(self.fc3(inp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Critic(nn.Module):\n",
    "\n",
    "    def __init__(self, state_size, action_size, seed, fcs1_units=256, fc2_units=128):\n",
    "        \"\"\" \n",
    "        PARAMETERS: \n",
    "            state_size (int): Dimension of a state\n",
    "            action_size (int): Dimension of an action\n",
    "            seed (int): Rand seed\n",
    "            fcs1_units (int): No. of nodes in the first hidden layer\n",
    "            fc2_units (int): No. of nodes in the second hidden layer\n",
    "        \"\"\"\n",
    "        super(Critic, self).__init__()\n",
    "        self.seed = torch.manual_seed(seed)\n",
    "        self.fcs1 = nn.Linear(state_size, fcs1_units)\n",
    "        self.fc2 = nn.Linear(fcs1_units+action_size, fc2_units)\n",
    "        self.fc3 = nn.Linear(fc2_units, 1)\n",
    "        self.reset_parameters()\n",
    "    def reset_parameters(self):\n",
    "        self.fcs1.weight.data.uniform_(*hidden_init(self.fcs1))\n",
    "        self.fc2.weight.data.uniform_(*hidden_init(self.fc2))\n",
    "        self.fc3.weight.data.uniform_(-3e-3, 3e-3)\n",
    "    def forward(self, state, action):\n",
    "        x = F.relu(self.fcs1(state))\n",
    "        x = torch.cat((x, action), dim=1)\n",
    "        x = F.relu(self.fc2(x))\n",
    "        return self.fc3(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReplayBuffer:\n",
    "    \n",
    "    def __init__(self, action_size, buffer_size, batch_size, seed):\n",
    "        \"\"\"\n",
    "        PARAMETERS:\n",
    "            buffer_size(int): Maximum buffer size\n",
    "            batch_size(int): Training batch size \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",
    "        exps = random.sample(self.memory, k=self.batch_size)\n",
    "        states = torch.from_numpy(np.vstack([e.state for e in exps if e is not None])).float().to(device)\n",
    "        actions = torch.from_numpy(np.vstack([e.action for e in exps if e is not None])).float().to(device)\n",
    "        rewards = torch.from_numpy(np.vstack([e.reward for e in exps if e is not None])).float().to(device)\n",
    "        next_states = torch.from_numpy(np.vstack([e.next_state for e in exps if e is not None])).float().to(device)\n",
    "        dones = torch.from_numpy(np.vstack([e.done for e in exps if e is not None]).astype(np.uint8)).float().to(device)\n",
    "        return (states, actions, rewards, next_states, dones)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OUNoise: #Ornstein-Uhlenbeck process\n",
    "\n",
    "    def __init__(self, size, seed, mu=0.0, theta=0.15, sigma=0.20):\n",
    "        self.size = size\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",
    "        rnd = np.random.standard_normal(self.size)\n",
    "        dx = self.theta * (self.mu - x) + self.sigma * rnd\n",
    "        self.state = x + dx\n",
    "        return self.state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Agent():\n",
    "    \n",
    "    def __init__(self, num_agents, state_size, action_size, buffer_size, batch_size, learn_every, update_every, random_seed):\n",
    "        \"\"\"\n",
    "        PARAMETERS:\n",
    "            state_size (int): dimension of each state\n",
    "            action_size (int): dimension of each action\n",
    "            random_seed (int): random seed\n",
    "        \"\"\"\n",
    "        self.num_agents = num_agents\n",
    "        self.state_size = state_size\n",
    "        self.action_size = action_size\n",
    "        self.learn_every = learn_every\n",
    "        self.update_every = update_every\n",
    "        self.seed = random.seed(random_seed)\n",
    "        self.buffer_size = buffer_size\n",
    "        self.batch_size = batch_size\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",
    "        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",
    "        self.noise = OUNoise((num_agents,action_size), random_seed)\n",
    "        self.memory = ReplayBuffer(action_size, self.buffer_size, self.batch_size, random_seed)\n",
    "    \n",
    "    def step(self, state, action, reward, next_state, done):\n",
    "        for i in range(self.num_agents):\n",
    "            self.memory.add(state[i,:], action[i,:], reward[i], next_state[i,:], done[i])\n",
    "        self.i_step += 1            \n",
    "        should_learn = (self.i_step % self.learn_every) == 0\n",
    "        should_update = (self.i_step % self.update_every) == 0\n",
    "        if len(self.memory) > self.batch_size:\n",
    "            if should_learn:\n",
    "                exps = self.memory.sample()\n",
    "                self.learn(exps, gamma)\n",
    "            if should_update:\n",
    "                self.soft_update(self.critic_local, self.critic_target, tau)\n",
    "                self.soft_update(self.actor_local, self.actor_target, tau) \n",
    "\n",
    "    def act(self, state, add_noise=True):\n",
    "        state = torch.from_numpy(state).float().to(device)\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.noise.sample()\n",
    "        return np.clip(action, -1, 1)\n",
    "\n",
    "    def reset(self):\n",
    "        self.noise.reset()\n",
    "        self.i_step=0\n",
    "\n",
    "    def learn(self, exps, gamma):\n",
    "        states, actions, rewards, next_states, dones = exps\n",
    "        actions_next = self.actor_target(next_states)\n",
    "        Q_targets_next = self.critic_target(next_states, actions_next)\n",
    "        Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))\n",
    "        Q_expected = self.critic_local(states, actions)\n",
    "        critic_loss = F.mse_loss(Q_expected, Q_targets)\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",
    "        self.actor_optimizer.zero_grad()\n",
    "        actor_loss.backward()\n",
    "        self.actor_optimizer.step()                    \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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Environment():\n",
    "    \n",
    "    def __init__(self,path):\n",
    "        self.env = UnityEnvironment(file_name=path);\n",
    "        self.brain_name = self.env.brain_names[0]\n",
    "        self.brain = self.env.brains[self.brain_name]\n",
    "        self.state_size = self.brain.vector_observation_space_size\n",
    "        self.action_size = self.brain.vector_action_space_size\n",
    "        info = self.env.reset(train_mode=True)[self.brain_name]\n",
    "        self.num_agents = len(info.agents)\n",
    "        \n",
    "    def close(self):\n",
    "        self.env.close()\n",
    "    \n",
    "    def reset(self,train=True):\n",
    "        info = self.env.reset(train_mode=train)[self.brain_name]\n",
    "        return info.vector_observations\n",
    "    \n",
    "    def step(self,action):\n",
    "        info = self.env.step(action)[self.brain_name]\n",
    "        state = info.vector_observations\n",
    "        reward = info.rewards\n",
    "        done = info.local_done\n",
    "        return state, reward, done, info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:unityagents:\n",
      "'Academy' started successfully!\n",
      "Unity Academy name: Academy\n",
      "        Number of Brains: 1\n",
      "        Number of External Brains : 1\n",
      "        Lesson number : 0\n",
      "        Reset Parameters :\n",
      "\t\tgoal_speed -> 1.0\n",
      "\t\tgoal_size -> 5.0\n",
      "Unity brain name: ReacherBrain\n",
      "        Number of Visual Observations (per agent): 0\n",
      "        Vector Observation space type: continuous\n",
      "        Vector Observation space size (per agent): 33\n",
      "        Number of stacked Vector Observation: 1\n",
      "        Vector Action space type: continuous\n",
      "        Vector Action space size (per agent): 4\n",
      "        Vector Action descriptions: , , , \n"
     ]
    }
   ],
   "source": [
    "env = Environment('Reacher20.app')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "buf_size = int(1e+5)\n",
    "gamma = 0.99\n",
    "tau = 1e-3\n",
    "weight_decay = 0\n",
    "lr_actor = 1e-4\n",
    "lr_critic = 1e-4\n",
    "update_step = 1\n",
    "learn_step  = 1\n",
    "rand_seed = 0\n",
    "\n",
    "num_episodes = 200\n",
    "\n",
    "session = str(int(time.time()))\n",
    "\n",
    "agent_config = {\n",
    "    'num_agents':env.num_agents,\n",
    "    'state_size':env.state_size,\n",
    "    'action_size':env.action_size,\n",
    "    'buffer_size':buf_size,\n",
    "    'batch_size':batch_size,\n",
    "    'learn_every':learn_step,\n",
    "    'update_every':update_step,\n",
    "    'random_seed':rand_seed\n",
    "}\n",
    "\n",
    "agent = Agent(**agent_config)\n",
    "\n",
    "def DDPG(n_episodes = num_episodes, max_t=1000):\n",
    "    scores_deque = deque(maxlen=100)\n",
    "    scores = []\n",
    "    max_score = 0\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "        states = env.reset()\n",
    "        agent.reset()\n",
    "        score = np.zeros(env.num_agents)\n",
    "        \n",
    "        while True:\n",
    "            actions = agent.act(states)\n",
    "            next_states, rewards, dones, _ = env.step(actions)\n",
    "            agent.step(states, actions, rewards, next_states, dones)\n",
    "            score = score + rewards\n",
    "            states = next_states\n",
    "            if np.any(dones):\n",
    "                break\n",
    "        \n",
    "        scores_deque.append(score)\n",
    "        scores.append(score)\n",
    "        avgscore_ep = np.mean(score)\n",
    "        avgscore_100 = np.mean(scores_deque)\n",
    "        print(f\"Episode: {i_episode}/{num_episodes}\\tScore: {avgscore_ep:.2f}\\tAvgScore: {avgscore_100:.2f}\")\n",
    "        if avgscore_100>=30.0:\n",
    "            print(f\"***Solved! Episodes taken: {i_episode:d} AvgScore: {avgscore_100:.3f}***\")\n",
    "            if avgscore_100 > max_score:\n",
    "                max_score = avgscore_100\n",
    "                file_tpl = 'savedmodels/checkpoint_{}_{}_episode{}_score{:.3f}.pth'\n",
    "                file_tpl_best = 'savedmodels/checkpoint_{}_{}_best.pth'\n",
    "                fileactor = file_tpl.format(session,'actor', i_episode, avgscore_100)\n",
    "                filecritic = file_tpl.format(session,'critic', i_episode, avgscore_100)\n",
    "                torch.save(agent.actor_local.state_dict(), fileactor)\n",
    "                torch.save(agent.critic_local.state_dict(), filecritic)\n",
    "                copyfile(fileactor, file_tpl_best.format(session,'actor'))\n",
    "                copyfile(filecritic, file_tpl_best.format(session,'critic'))         \n",
    "\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode: 1/200\tScore: 0.65\tAvgScore: 0.65\n",
      "Episode: 2/200\tScore: 0.62\tAvgScore: 0.64\n",
      "Episode: 3/200\tScore: 0.51\tAvgScore: 0.60\n",
      "Episode: 4/200\tScore: 0.65\tAvgScore: 0.61\n",
      "Episode: 5/200\tScore: 0.60\tAvgScore: 0.61\n",
      "Episode: 6/200\tScore: 0.81\tAvgScore: 0.64\n",
      "Episode: 7/200\tScore: 0.94\tAvgScore: 0.68\n",
      "Episode: 8/200\tScore: 1.17\tAvgScore: 0.74\n",
      "Episode: 9/200\tScore: 1.17\tAvgScore: 0.79\n",
      "Episode: 10/200\tScore: 1.37\tAvgScore: 0.85\n",
      "Episode: 11/200\tScore: 2.24\tAvgScore: 0.98\n",
      "Episode: 12/200\tScore: 1.83\tAvgScore: 1.05\n",
      "Episode: 13/200\tScore: 1.84\tAvgScore: 1.11\n",
      "Episode: 14/200\tScore: 1.90\tAvgScore: 1.16\n",
      "Episode: 15/200\tScore: 1.90\tAvgScore: 1.21\n",
      "Episode: 16/200\tScore: 2.38\tAvgScore: 1.29\n",
      "Episode: 17/200\tScore: 2.58\tAvgScore: 1.36\n",
      "Episode: 18/200\tScore: 2.06\tAvgScore: 1.40\n",
      "Episode: 19/200\tScore: 3.08\tAvgScore: 1.49\n",
      "Episode: 20/200\tScore: 3.48\tAvgScore: 1.59\n",
      "Episode: 21/200\tScore: 5.09\tAvgScore: 1.76\n",
      "Episode: 22/200\tScore: 4.80\tAvgScore: 1.89\n",
      "Episode: 23/200\tScore: 5.22\tAvgScore: 2.04\n",
      "Episode: 24/200\tScore: 6.40\tAvgScore: 2.22\n",
      "Episode: 25/200\tScore: 8.15\tAvgScore: 2.46\n",
      "Episode: 26/200\tScore: 8.10\tAvgScore: 2.68\n",
      "Episode: 27/200\tScore: 9.39\tAvgScore: 2.92\n",
      "Episode: 28/200\tScore: 6.95\tAvgScore: 3.07\n",
      "Episode: 29/200\tScore: 10.12\tAvgScore: 3.31\n",
      "Episode: 30/200\tScore: 13.10\tAvgScore: 3.64\n",
      "Episode: 31/200\tScore: 15.27\tAvgScore: 4.01\n",
      "Episode: 32/200\tScore: 15.94\tAvgScore: 4.39\n",
      "Episode: 33/200\tScore: 18.87\tAvgScore: 4.82\n",
      "Episode: 34/200\tScore: 18.93\tAvgScore: 5.24\n",
      "Episode: 35/200\tScore: 19.87\tAvgScore: 5.66\n",
      "Episode: 36/200\tScore: 22.34\tAvgScore: 6.12\n",
      "Episode: 37/200\tScore: 25.16\tAvgScore: 6.64\n",
      "Episode: 38/200\tScore: 29.74\tAvgScore: 7.24\n",
      "Episode: 39/200\tScore: 32.42\tAvgScore: 7.89\n",
      "Episode: 40/200\tScore: 33.24\tAvgScore: 8.52\n",
      "Episode: 41/200\tScore: 35.71\tAvgScore: 9.19\n",
      "Episode: 42/200\tScore: 34.30\tAvgScore: 9.78\n",
      "Episode: 43/200\tScore: 36.76\tAvgScore: 10.41\n",
      "Episode: 44/200\tScore: 37.34\tAvgScore: 11.02\n",
      "Episode: 45/200\tScore: 37.67\tAvgScore: 11.62\n",
      "Episode: 46/200\tScore: 37.02\tAvgScore: 12.17\n",
      "Episode: 47/200\tScore: 37.41\tAvgScore: 12.70\n",
      "Episode: 48/200\tScore: 36.89\tAvgScore: 13.21\n",
      "Episode: 49/200\tScore: 37.41\tAvgScore: 13.70\n",
      "Episode: 50/200\tScore: 37.46\tAvgScore: 14.18\n",
      "Episode: 51/200\tScore: 37.50\tAvgScore: 14.64\n",
      "Episode: 52/200\tScore: 37.81\tAvgScore: 15.08\n",
      "Episode: 53/200\tScore: 36.14\tAvgScore: 15.48\n",
      "Episode: 54/200\tScore: 37.51\tAvgScore: 15.89\n",
      "Episode: 55/200\tScore: 37.07\tAvgScore: 16.27\n",
      "Episode: 56/200\tScore: 36.79\tAvgScore: 16.64\n",
      "Episode: 57/200\tScore: 37.30\tAvgScore: 17.00\n",
      "Episode: 58/200\tScore: 37.06\tAvgScore: 17.35\n",
      "Episode: 59/200\tScore: 36.68\tAvgScore: 17.67\n",
      "Episode: 60/200\tScore: 36.38\tAvgScore: 17.99\n",
      "Episode: 61/200\tScore: 37.24\tAvgScore: 18.30\n",
      "Episode: 62/200\tScore: 37.49\tAvgScore: 18.61\n",
      "Episode: 63/200\tScore: 36.89\tAvgScore: 18.90\n",
      "Episode: 64/200\tScore: 36.74\tAvgScore: 19.18\n",
      "Episode: 65/200\tScore: 36.54\tAvgScore: 19.45\n",
      "Episode: 66/200\tScore: 36.95\tAvgScore: 19.71\n",
      "Episode: 67/200\tScore: 36.86\tAvgScore: 19.97\n",
      "Episode: 68/200\tScore: 36.88\tAvgScore: 20.22\n",
      "Episode: 69/200\tScore: 37.14\tAvgScore: 20.46\n",
      "Episode: 70/200\tScore: 35.47\tAvgScore: 20.68\n",
      "Episode: 71/200\tScore: 35.36\tAvgScore: 20.88\n",
      "Episode: 72/200\tScore: 35.24\tAvgScore: 21.08\n",
      "Episode: 73/200\tScore: 36.26\tAvgScore: 21.29\n",
      "Episode: 74/200\tScore: 35.92\tAvgScore: 21.49\n",
      "Episode: 75/200\tScore: 35.60\tAvgScore: 21.68\n",
      "Episode: 76/200\tScore: 36.64\tAvgScore: 21.87\n",
      "Episode: 77/200\tScore: 36.63\tAvgScore: 22.06\n",
      "Episode: 78/200\tScore: 35.22\tAvgScore: 22.23\n",
      "Episode: 79/200\tScore: 35.64\tAvgScore: 22.40\n",
      "Episode: 80/200\tScore: 36.39\tAvgScore: 22.58\n",
      "Episode: 81/200\tScore: 35.31\tAvgScore: 22.73\n",
      "Episode: 82/200\tScore: 34.61\tAvgScore: 22.88\n",
      "Episode: 83/200\tScore: 36.05\tAvgScore: 23.04\n",
      "Episode: 84/200\tScore: 35.69\tAvgScore: 23.19\n",
      "Episode: 85/200\tScore: 36.50\tAvgScore: 23.35\n",
      "Episode: 86/200\tScore: 34.94\tAvgScore: 23.48\n",
      "Episode: 87/200\tScore: 37.07\tAvgScore: 23.64\n",
      "Episode: 88/200\tScore: 35.71\tAvgScore: 23.77\n",
      "Episode: 89/200\tScore: 36.84\tAvgScore: 23.92\n",
      "Episode: 90/200\tScore: 36.04\tAvgScore: 24.06\n",
      "Episode: 91/200\tScore: 36.55\tAvgScore: 24.19\n",
      "Episode: 92/200\tScore: 36.75\tAvgScore: 24.33\n",
      "Episode: 93/200\tScore: 37.00\tAvgScore: 24.47\n",
      "Episode: 94/200\tScore: 36.53\tAvgScore: 24.59\n",
      "Episode: 95/200\tScore: 35.76\tAvgScore: 24.71\n",
      "Episode: 96/200\tScore: 36.66\tAvgScore: 24.84\n",
      "Episode: 97/200\tScore: 34.81\tAvgScore: 24.94\n",
      "Episode: 98/200\tScore: 34.36\tAvgScore: 25.03\n",
      "Episode: 99/200\tScore: 35.55\tAvgScore: 25.14\n",
      "Episode: 100/200\tScore: 36.04\tAvgScore: 25.25\n",
      "Episode: 101/200\tScore: 34.70\tAvgScore: 25.59\n",
      "Episode: 102/200\tScore: 34.61\tAvgScore: 25.93\n",
      "Episode: 103/200\tScore: 35.91\tAvgScore: 26.28\n",
      "Episode: 104/200\tScore: 35.22\tAvgScore: 26.63\n",
      "Episode: 105/200\tScore: 36.00\tAvgScore: 26.98\n",
      "Episode: 106/200\tScore: 35.30\tAvgScore: 27.33\n",
      "Episode: 107/200\tScore: 36.73\tAvgScore: 27.69\n",
      "Episode: 108/200\tScore: 35.52\tAvgScore: 28.03\n",
      "Episode: 109/200\tScore: 36.28\tAvgScore: 28.38\n",
      "Episode: 110/200\tScore: 36.04\tAvgScore: 28.73\n",
      "Episode: 111/200\tScore: 36.40\tAvgScore: 29.07\n",
      "Episode: 112/200\tScore: 35.88\tAvgScore: 29.41\n",
      "Episode: 113/200\tScore: 36.44\tAvgScore: 29.76\n",
      "Episode: 114/200\tScore: 35.41\tAvgScore: 30.09\n",
      "***Solved! Episodes taken: 114 AvgScore: 30.091***\n",
      "Episode: 115/200\tScore: 35.12\tAvgScore: 30.42\n",
      "***Solved! Episodes taken: 115 AvgScore: 30.423***\n",
      "Episode: 116/200\tScore: 36.16\tAvgScore: 30.76\n",
      "***Solved! Episodes taken: 116 AvgScore: 30.761***\n",
      "Episode: 117/200\tScore: 36.23\tAvgScore: 31.10\n",
      "***Solved! Episodes taken: 117 AvgScore: 31.098***\n",
      "Episode: 118/200\tScore: 35.80\tAvgScore: 31.43\n",
      "***Solved! Episodes taken: 118 AvgScore: 31.435***\n",
      "Episode: 119/200\tScore: 35.84\tAvgScore: 31.76\n",
      "***Solved! Episodes taken: 119 AvgScore: 31.763***\n",
      "Episode: 120/200\tScore: 36.62\tAvgScore: 32.09\n",
      "***Solved! Episodes taken: 120 AvgScore: 32.094***\n",
      "Episode: 121/200\tScore: 37.22\tAvgScore: 32.42\n",
      "***Solved! Episodes taken: 121 AvgScore: 32.415***\n",
      "Episode: 122/200\tScore: 35.64\tAvgScore: 32.72\n",
      "***Solved! Episodes taken: 122 AvgScore: 32.724***\n",
      "Episode: 123/200\tScore: 35.66\tAvgScore: 33.03\n",
      "***Solved! Episodes taken: 123 AvgScore: 33.028***\n",
      "Episode: 124/200\tScore: 35.44\tAvgScore: 33.32\n",
      "***Solved! Episodes taken: 124 AvgScore: 33.318***\n",
      "Episode: 125/200\tScore: 35.30\tAvgScore: 33.59\n",
      "***Solved! Episodes taken: 125 AvgScore: 33.590***\n",
      "Episode: 126/200\tScore: 36.04\tAvgScore: 33.87\n",
      "***Solved! Episodes taken: 126 AvgScore: 33.869***\n",
      "Episode: 127/200\tScore: 36.21\tAvgScore: 34.14\n",
      "***Solved! Episodes taken: 127 AvgScore: 34.137***\n",
      "Episode: 128/200\tScore: 36.79\tAvgScore: 34.44\n",
      "***Solved! Episodes taken: 128 AvgScore: 34.436***\n",
      "Episode: 129/200\tScore: 35.40\tAvgScore: 34.69\n",
      "***Solved! Episodes taken: 129 AvgScore: 34.688***\n",
      "Episode: 130/200\tScore: 37.23\tAvgScore: 34.93\n",
      "***Solved! Episodes taken: 130 AvgScore: 34.930***\n",
      "Episode: 131/200\tScore: 36.89\tAvgScore: 35.15\n",
      "***Solved! Episodes taken: 131 AvgScore: 35.146***\n",
      "Episode: 132/200\tScore: 37.06\tAvgScore: 35.36\n",
      "***Solved! Episodes taken: 132 AvgScore: 35.357***\n",
      "Episode: 133/200\tScore: 36.77\tAvgScore: 35.54\n",
      "***Solved! Episodes taken: 133 AvgScore: 35.536***\n",
      "Episode: 134/200\tScore: 36.75\tAvgScore: 35.71\n",
      "***Solved! Episodes taken: 134 AvgScore: 35.714***\n",
      "Episode: 135/200\tScore: 35.79\tAvgScore: 35.87\n",
      "***Solved! Episodes taken: 135 AvgScore: 35.873***\n",
      "Episode: 136/200\tScore: 35.54\tAvgScore: 36.01\n",
      "***Solved! Episodes taken: 136 AvgScore: 36.005***\n",
      "Episode: 137/200\tScore: 36.29\tAvgScore: 36.12\n",
      "***Solved! Episodes taken: 137 AvgScore: 36.117***\n",
      "Episode: 138/200\tScore: 36.21\tAvgScore: 36.18\n",
      "***Solved! Episodes taken: 138 AvgScore: 36.181***\n",
      "Episode: 139/200\tScore: 36.04\tAvgScore: 36.22\n",
      "***Solved! Episodes taken: 139 AvgScore: 36.218***\n",
      "Episode: 140/200\tScore: 35.53\tAvgScore: 36.24\n",
      "***Solved! Episodes taken: 140 AvgScore: 36.241***\n",
      "Episode: 141/200\tScore: 36.09\tAvgScore: 36.24\n",
      "***Solved! Episodes taken: 141 AvgScore: 36.244***\n",
      "Episode: 142/200\tScore: 36.41\tAvgScore: 36.27\n",
      "***Solved! Episodes taken: 142 AvgScore: 36.266***\n",
      "Episode: 143/200\tScore: 36.36\tAvgScore: 36.26\n",
      "***Solved! Episodes taken: 143 AvgScore: 36.262***\n",
      "Episode: 144/200\tScore: 36.04\tAvgScore: 36.25\n",
      "***Solved! Episodes taken: 144 AvgScore: 36.248***\n",
      "Episode: 145/200\tScore: 36.66\tAvgScore: 36.24\n",
      "***Solved! Episodes taken: 145 AvgScore: 36.238***\n",
      "Episode: 146/200\tScore: 36.50\tAvgScore: 36.23\n",
      "***Solved! Episodes taken: 146 AvgScore: 36.233***\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode: 147/200\tScore: 35.49\tAvgScore: 36.21\n",
      "***Solved! Episodes taken: 147 AvgScore: 36.214***\n",
      "Episode: 148/200\tScore: 36.70\tAvgScore: 36.21\n",
      "***Solved! Episodes taken: 148 AvgScore: 36.212***\n",
      "Episode: 149/200\tScore: 35.11\tAvgScore: 36.19\n",
      "***Solved! Episodes taken: 149 AvgScore: 36.189***\n",
      "Episode: 150/200\tScore: 35.47\tAvgScore: 36.17\n",
      "***Solved! Episodes taken: 150 AvgScore: 36.169***\n",
      "Episode: 151/200\tScore: 35.84\tAvgScore: 36.15\n",
      "***Solved! Episodes taken: 151 AvgScore: 36.153***\n",
      "Episode: 152/200\tScore: 36.40\tAvgScore: 36.14\n",
      "***Solved! Episodes taken: 152 AvgScore: 36.138***\n",
      "Episode: 153/200\tScore: 36.78\tAvgScore: 36.14\n",
      "***Solved! Episodes taken: 153 AvgScore: 36.145***\n",
      "Episode: 154/200\tScore: 35.80\tAvgScore: 36.13\n",
      "***Solved! Episodes taken: 154 AvgScore: 36.128***\n",
      "Episode: 155/200\tScore: 35.55\tAvgScore: 36.11\n",
      "***Solved! Episodes taken: 155 AvgScore: 36.113***\n",
      "Episode: 156/200\tScore: 35.47\tAvgScore: 36.10\n",
      "***Solved! Episodes taken: 156 AvgScore: 36.099***\n",
      "Episode: 157/200\tScore: 34.44\tAvgScore: 36.07\n",
      "***Solved! Episodes taken: 157 AvgScore: 36.071***\n",
      "Episode: 158/200\tScore: 35.96\tAvgScore: 36.06\n",
      "***Solved! Episodes taken: 158 AvgScore: 36.060***\n",
      "Episode: 159/200\tScore: 35.27\tAvgScore: 36.05\n",
      "***Solved! Episodes taken: 159 AvgScore: 36.046***\n",
      "Episode: 160/200\tScore: 36.84\tAvgScore: 36.05\n",
      "***Solved! Episodes taken: 160 AvgScore: 36.050***\n",
      "Episode: 161/200\tScore: 36.62\tAvgScore: 36.04\n",
      "***Solved! Episodes taken: 161 AvgScore: 36.044***\n",
      "Episode: 162/200\tScore: 36.46\tAvgScore: 36.03\n",
      "***Solved! Episodes taken: 162 AvgScore: 36.034***\n",
      "Episode: 163/200\tScore: 34.53\tAvgScore: 36.01\n",
      "***Solved! Episodes taken: 163 AvgScore: 36.010***\n",
      "Episode: 164/200\tScore: 36.66\tAvgScore: 36.01\n",
      "***Solved! Episodes taken: 164 AvgScore: 36.009***\n",
      "Episode: 165/200\tScore: 36.86\tAvgScore: 36.01\n",
      "***Solved! Episodes taken: 165 AvgScore: 36.013***\n",
      "Episode: 166/200\tScore: 37.02\tAvgScore: 36.01\n",
      "***Solved! Episodes taken: 166 AvgScore: 36.013***\n",
      "Episode: 167/200\tScore: 36.93\tAvgScore: 36.01\n",
      "***Solved! Episodes taken: 167 AvgScore: 36.014***\n",
      "Episode: 168/200\tScore: 36.65\tAvgScore: 36.01\n",
      "***Solved! Episodes taken: 168 AvgScore: 36.012***\n",
      "Episode: 169/200\tScore: 35.27\tAvgScore: 35.99\n",
      "***Solved! Episodes taken: 169 AvgScore: 35.993***\n",
      "Episode: 170/200\tScore: 36.41\tAvgScore: 36.00\n",
      "***Solved! Episodes taken: 170 AvgScore: 36.002***\n",
      "Episode: 171/200\tScore: 36.09\tAvgScore: 36.01\n",
      "***Solved! Episodes taken: 171 AvgScore: 36.010***\n",
      "Episode: 172/200\tScore: 35.87\tAvgScore: 36.02\n",
      "***Solved! Episodes taken: 172 AvgScore: 36.016***\n",
      "Episode: 173/200\tScore: 35.63\tAvgScore: 36.01\n",
      "***Solved! Episodes taken: 173 AvgScore: 36.010***\n",
      "Episode: 174/200\tScore: 36.80\tAvgScore: 36.02\n",
      "***Solved! Episodes taken: 174 AvgScore: 36.018***\n",
      "Episode: 175/200\tScore: 36.44\tAvgScore: 36.03\n",
      "***Solved! Episodes taken: 175 AvgScore: 36.027***\n",
      "Episode: 176/200\tScore: 36.41\tAvgScore: 36.02\n",
      "***Solved! Episodes taken: 176 AvgScore: 36.025***\n",
      "Episode: 177/200\tScore: 36.52\tAvgScore: 36.02\n",
      "***Solved! Episodes taken: 177 AvgScore: 36.023***\n",
      "Episode: 178/200\tScore: 36.63\tAvgScore: 36.04\n",
      "***Solved! Episodes taken: 178 AvgScore: 36.037***\n",
      "Episode: 179/200\tScore: 35.81\tAvgScore: 36.04\n",
      "***Solved! Episodes taken: 179 AvgScore: 36.039***\n",
      "Episode: 180/200\tScore: 37.14\tAvgScore: 36.05\n",
      "***Solved! Episodes taken: 180 AvgScore: 36.047***\n",
      "Episode: 181/200\tScore: 34.98\tAvgScore: 36.04\n",
      "***Solved! Episodes taken: 181 AvgScore: 36.043***\n",
      "Episode: 182/200\tScore: 36.28\tAvgScore: 36.06\n",
      "***Solved! Episodes taken: 182 AvgScore: 36.060***\n",
      "Episode: 183/200\tScore: 36.54\tAvgScore: 36.06\n",
      "***Solved! Episodes taken: 183 AvgScore: 36.065***\n",
      "Episode: 184/200\tScore: 34.93\tAvgScore: 36.06\n",
      "***Solved! Episodes taken: 184 AvgScore: 36.057***\n",
      "Episode: 185/200\tScore: 35.71\tAvgScore: 36.05\n",
      "***Solved! Episodes taken: 185 AvgScore: 36.049***\n",
      "Episode: 186/200\tScore: 34.49\tAvgScore: 36.04\n",
      "***Solved! Episodes taken: 186 AvgScore: 36.045***\n",
      "Episode: 187/200\tScore: 36.28\tAvgScore: 36.04\n",
      "***Solved! Episodes taken: 187 AvgScore: 36.037***\n",
      "Episode: 188/200\tScore: 36.06\tAvgScore: 36.04\n",
      "***Solved! Episodes taken: 188 AvgScore: 36.041***\n",
      "Episode: 189/200\tScore: 35.26\tAvgScore: 36.02\n",
      "***Solved! Episodes taken: 189 AvgScore: 36.025***\n",
      "Episode: 190/200\tScore: 37.12\tAvgScore: 36.04\n",
      "***Solved! Episodes taken: 190 AvgScore: 36.035***\n",
      "Episode: 191/200\tScore: 36.53\tAvgScore: 36.04\n",
      "***Solved! Episodes taken: 191 AvgScore: 36.035***\n",
      "Episode: 192/200\tScore: 34.80\tAvgScore: 36.02\n",
      "***Solved! Episodes taken: 192 AvgScore: 36.016***\n",
      "Episode: 193/200\tScore: 35.26\tAvgScore: 36.00\n",
      "***Solved! Episodes taken: 193 AvgScore: 35.998***\n",
      "Episode: 194/200\tScore: 35.16\tAvgScore: 35.98\n",
      "***Solved! Episodes taken: 194 AvgScore: 35.985***\n",
      "Episode: 195/200\tScore: 35.00\tAvgScore: 35.98\n",
      "***Solved! Episodes taken: 195 AvgScore: 35.977***\n",
      "Episode: 196/200\tScore: 35.91\tAvgScore: 35.97\n",
      "***Solved! Episodes taken: 196 AvgScore: 35.970***\n",
      "Episode: 197/200\tScore: 37.34\tAvgScore: 35.99\n",
      "***Solved! Episodes taken: 197 AvgScore: 35.995***\n",
      "Episode: 198/200\tScore: 36.81\tAvgScore: 36.02\n",
      "***Solved! Episodes taken: 198 AvgScore: 36.019***\n",
      "Episode: 199/200\tScore: 36.38\tAvgScore: 36.03\n",
      "***Solved! Episodes taken: 199 AvgScore: 36.028***\n",
      "Episode: 200/200\tScore: 36.06\tAvgScore: 36.03\n",
      "***Solved! Episodes taken: 200 AvgScore: 36.028***\n"
     ]
    }
   ],
   "source": [
    "scores = DDPG()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "perf = fig.add_subplot(111)\n",
    "plt.plot(np.arange(1, len(scores)+1), scores)\n",
    "plt.ylabel('Score')\n",
    "plt.xlabel('Episode Number')\n",
    "plt.rcParams[\"figure.figsize\"] = [18.0, 12.0]\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "drlnd",
   "language": "python",
   "name": "drlnd"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}