agent_testing_notebook.ipynb

{
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    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'numpy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01magent\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Agent\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mreinforcement_learning\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolicies\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m EpsilonGreedyPolicy\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'numpy'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from agent import Agent\n",
    "from reinforcement_learning.policies import EpsilonGreedyPolicy\n",
    "\n",
    "EPOCHS = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the agent and the policy\n",
    "egreedypolicity = EpsilonGreedyPolicy(0.1)\n",
    "agent = Agent(10, 10, egreedypolicity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the agent\n",
    "for epoch in range(EPOCHS):\n",
    "    action = agent.get_action()\n",
    "    reward = np.random.randn() + action  # Reward is a random number with a bias towards the action taken\n",
    "    agent.update(action, reward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.00993218  1.31472509  1.85931189  2.92290795  4.36860127  4.95482366\n",
      "  5.93906745  7.09288159  7.94442688  9.02432172]\n"
     ]
    }
   ],
   "source": [
    "# Print the agent's action values (should be close to the true action values)\n",
    "print(agent.action_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 14.   7.  17.   6.  12.  11.   9.  11.  15. 898.]\n"
     ]
    }
   ],
   "source": [
    "# Print the agent's action counts (should be higher for actions with higher values)\n",
    "print(agent.action_counts)"
   ]
  }
 ],
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