ReacherWithTrainedWeights.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "from collections import namedtuple, deque\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "import numpy as np\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ddpg_agent import DDPGAgent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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 = UnityEnvironment(file_name='Reacher_Linux1/Reacher.x86_64')\n",
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]\n",
    "env_info = env.reset(train_mode=False)[brain_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of agents: 1\n",
      "Size of each action: 4\n"
     ]
    }
   ],
   "source": [
    "# number of agents\n",
    "num_agents = len(env_info.agents)\n",
    "print('Number of agents:', num_agents)\n",
    "\n",
    "# size of each action\n",
    "action_size = brain.vector_action_space_size\n",
    "print('Size of each action:', action_size)\n",
    "\n",
    "# examine the state space \n",
    "states = env_info.vector_observations\n",
    "state_size = states.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_actor = state_size\n",
    "in_critic = state_size + action_size\n",
    "\n",
    "hidden_in_actor = 256\n",
    "hidden_out_actor = 128\n",
    "hidden_in_critic = 256\n",
    "hidden_out_critic = 128\n",
    "\n",
    "out_actor = action_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model import DDPGNet\n",
    "target_actor = DDPGNet(in_actor, hidden_in_actor, hidden_out_actor, out_actor, actor = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DDPGNet(\n",
       "  (fc1): Linear(in_features=33, out_features=256, bias=True)\n",
       "  (fc2): Linear(in_features=256, out_features=128, bias=True)\n",
       "  (fc3): Linear(in_features=128, out_features=4, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_actor.load_state_dict(torch.load('best_checkpoint500.pth'))\n",
    "target_actor.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def actor_act(target_actor, state, eps = 0.0):\n",
    "    state = torch.from_numpy(state).float().unsqueeze(0).to(device)\n",
    "    target_actor.eval()\n",
    "    with torch.no_grad():\n",
    "        action_values = target_actor(state)\n",
    "    target_actor.train()\n",
    "    action_vals_npy = action_values.cpu().data.numpy()\n",
    "    return np.clip(action_vals_npy, -1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the weights from file\n",
    "env_info = env.reset(train_mode = False)[brain_name]\n",
    "state = env_info.vector_observations[0]\n",
    "score = 0\n",
    "while True:\n",
    "    action = actor_act(target_actor, state, eps = 0.0) #target_actor.act(state, eps)\n",
    "    env_info = env.step(action)[brain_name]\n",
    "    next_state = env_info.vector_observations[0]\n",
    "    reward = env_info.rewards[0]\n",
    "    done = env_info.local_done[0]\n",
    "    #agent.step(state, action, reward, next_state, done)\n",
    "    state = next_state\n",
    "    score += reward\n",
    "    if done:\n",
    "        break\n",
    "    \n",
    "print(\"Score: {}\".format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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