testNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "from gym_minigrid.wrappers import ImgObsWrapper\n",
    "from mini_behavior.utils.wrappers import MiniBHFullyObsWrapper\n",
    "from mini_behavior.register import register\n",
    "from algorithms.APT_PPO import APT_PPO\n",
    "import mini_behavior\n",
    "from stable_baselines3 import PPO\n",
    "import numpy as np\n",
    "from stable_baselines3.common.env_util import make_vec_env\n",
    "from stable_baselines3.common.torch_layers import BaseFeaturesExtractor\n",
    "from stable_baselines3.common.callbacks import StopTrainingOnMaxEpisodes\n",
    "from stable_baselines3.common.vec_env.subproc_vec_env import SubprocVecEnv\n",
    "from stable_baselines3.common.vec_env.dummy_vec_env import DummyVecEnv\n",
    "import torch.nn as nn\n",
    "import torch\n",
    "from env_wrapper import CustomObservationWrapper\n",
    "import argparse\n",
    "\n",
    "class MinigridFeaturesExtractor(BaseFeaturesExtractor):\n",
    "    def __init__(self, observation_space: gym.Space, features_dim: int = 512, normalized_image: bool = False) -> None:\n",
    "        super().__init__(observation_space, features_dim)\n",
    "        n_input_channels = observation_space.shape[0]\n",
    "        self.cnn = nn.Sequential(\n",
    "            nn.Conv2d(n_input_channels, 32, (2, 2)),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(32, 32, (2, 2)),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(32, 64, (2, 2)),\n",
    "            nn.ReLU(),\n",
    "            nn.Flatten(),\n",
    "        )\n",
    "\n",
    "        # Compute shape by doing one forward pass\n",
    "        with torch.no_grad():\n",
    "            n_flatten = self.cnn(torch.as_tensor(observation_space.sample()[None]).float()).shape[1]\n",
    "\n",
    "        self.linear = nn.Sequential(nn.Linear(n_flatten, features_dim), nn.ReLU())\n",
    "\n",
    "    def forward(self, observations: torch.Tensor) -> torch.Tensor:\n",
    "        return self.linear(self.cnn(observations))\n",
    "\n",
    "TASK = 'MultiToy'\n",
    "PARTIAL_OBS = True\n",
    "ROOM_SIZE = 10\n",
    "MAX_STEPS = 1000\n",
    "TOTAL_TIMESTEPS = 1e5\n",
    "DENSE_REWARD = False\n",
    "POLICY_TYPE = 'CnnPolicy'\n",
    "NUM_ENVS = 8\n",
    "NUM_STEPS = 125\n",
    "env_name = f\"MiniGrid-{TASK}-{ROOM_SIZE}x{ROOM_SIZE}-N2-v0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "env_kwargs = {\"room_size\": ROOM_SIZE, \"max_steps\": MAX_STEPS, \"exploration_type\": \"ATP\"}\n",
    "def get_single_env() -> gym.Env:\n",
    "    '''\n",
    "    policy_kwargs = dict(\n",
    "    features_extractor_class=MinigridFeaturesExtractor,\n",
    "    features_extractor_kwargs=dict(features_dim=128)\n",
    ")\n",
    "    '''\n",
    "\n",
    "    # Env wrapping\n",
    "    env_name = f\"MiniGrid-{TASK}-{ROOM_SIZE}x{ROOM_SIZE}-N2-v0\"\n",
    "\n",
    "    kwargs = {\"room_size\": ROOM_SIZE, \"max_steps\": MAX_STEPS}\n",
    "\n",
    "\n",
    "    register(\n",
    "        id=env_name,\n",
    "        entry_point=f'mini_behavior.envs:{TASK}Env',\n",
    "        kwargs=kwargs\n",
    "    )\n",
    "\n",
    "    '''\n",
    "    config = {\n",
    "        \"policy_type\": POLICY_TYPE,\n",
    "        \"total_timesteps\": TOTAL_TIMESTEPS,\n",
    "        \"env_name\": env_name,\n",
    "    }\n",
    "    '''\n",
    "    \n",
    "    env = gym.make(env_name)\n",
    "    env = CustomObservationWrapper(env)\n",
    "\n",
    "    return env\n",
    "\n",
    "\n",
    "def init_env(num_envs: int):\n",
    "\n",
    "    env_fns = [lambda: get_single_env() for _ in range(num_envs)]\n",
    "    if num_envs == 1:\n",
    "        return DummyVecEnv(env_fns)\n",
    "    else:\n",
    "        return SubprocVecEnv(env_fns)\n",
    "    \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Observation Space ===\n",
      "Shape: (59,)\n",
      "Type: float32\n"
     ]
    }
   ],
   "source": [
    "env = get_single_env()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5. 6. 0. 3. 3. 0. 0. 0. 0. 1. 7. 0. 0. 0. 3. 2. 0. 0. 0. 4. 1. 0. 0. 0.\n",
      " 0. 0. 6. 4. 1. 0. 0. 0. 0. 2. 1. 0. 0. 0. 0. 0. 0. 8. 6. 1. 0. 0. 0. 3.\n",
      " 5. 0. 0. 0. 2. 6. 0. 0. 0. 0. 0.]\n",
      "{'alligator_busy_box_0': {'infovofrobot': False, 'inhandofrobot': False, 'inreachofrobot': False, 'toggled': False}, 'ball_0': {'infovofrobot': False, 'inhandofrobot': False, 'inreachofrobot': False}, 'beach_ball_0': {'infovofrobot': False, 'inhandofrobot': False, 'inreachofrobot': False}, 'music_toy_0': {'infovofrobot': False, 'inhandofrobot': False, 'inreachofrobot': False, 'noise': False, 'toggled': False}, 'farm_toy_0': {'infovofrobot': True, 'inhandofrobot': False, 'inreachofrobot': False, 'popup': False, 'toggled': False}, 'piggie_bank_0': {'infovofrobot': False, 'inhandofrobot': False, 'inreachofrobot': False, 'noise': False, 'open': False, 'toggled': False}, 'rattle_0': {'infovofrobot': True, 'inhandofrobot': False, 'inreachofrobot': False, 'noise': False}, 'red_spiky_ball_0': {'infovofrobot': False, 'inhandofrobot': False, 'inreachofrobot': False}, 'winnie_cabinet_0': {'infovofrobot': False, 'inhandofrobot': False, 'inreachofrobot': False, 'open': False, 'toggled': False}}\n"
     ]
    }
   ],
   "source": [
    "print(env.gen_obs())\n",
    "objs_dict = {}\n",
    "for obj_type in env.objs.values():\n",
    "    for obj in obj_type:\n",
    "        states_dict = {}\n",
    "        for state_value in obj.states:\n",
    "            if not isinstance(obj.states[state_value], RelativeObjectState):\n",
    "                state = obj.states[state_value].get_value(env)\n",
    "                states_dict[state_value] = state\n",
    "        objs_dict[obj.name] = states_dict\n",
    "print(objs_dict)\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alligator_busy_box': [<mini_behavior.utils.objects_base.WorldObj object at 0x1059ae8e0>], 'ball': [<mini_behavior.utils.objects_base.WorldObj object at 0x1059ae610>], 'beach_ball': [<mini_behavior.utils.objects_base.WorldObj object at 0x1059aefa0>], 'music_toy': [<mini_behavior.utils.objects_base.WorldObj object at 0x105937670>], 'farm_toy': [<mini_behavior.utils.objects_base.WorldObj object at 0x14f78fe20>], 'piggie_bank': [<mini_behavior.utils.objects_base.WorldObj object at 0x14f78ffd0>], 'rattle': [<mini_behavior.utils.objects_base.WorldObj object at 0x14f78fcd0>], 'red_spiky_ball': [<mini_behavior.utils.objects_base.WorldObj object at 0x14f7915e0>], 'winnie_cabinet': [<mini_behavior.utils.objects_base.WorldObj object at 0x14f791820>]}\n",
      "alligator_busy_box_0\n",
      "\n",
      "infovofrobot: False\n",
      "inhandofrobot: False\n",
      "inreachofrobot: False\n",
      "toggled: False\n",
      "ball_0\n",
      "\n",
      "infovofrobot: False\n",
      "inhandofrobot: False\n",
      "inreachofrobot: False\n",
      "beach_ball_0\n",
      "\n",
      "infovofrobot: False\n",
      "inhandofrobot: False\n",
      "inreachofrobot: False\n",
      "music_toy_0\n",
      "\n",
      "infovofrobot: False\n",
      "inhandofrobot: False\n",
      "inreachofrobot: False\n",
      "noise: False\n",
      "toggled: False\n",
      "farm_toy_0\n",
      "\n",
      "infovofrobot: True\n",
      "inhandofrobot: False\n",
      "inreachofrobot: False\n",
      "popup: False\n",
      "toggled: False\n",
      "piggie_bank_0\n",
      "\n",
      "infovofrobot: False\n",
      "inhandofrobot: False\n",
      "inreachofrobot: False\n",
      "noise: False\n",
      "open: False\n",
      "toggled: False\n",
      "rattle_0\n",
      "\n",
      "infovofrobot: True\n",
      "inhandofrobot: False\n",
      "inreachofrobot: False\n",
      "noise: False\n",
      "red_spiky_ball_0\n",
      "\n",
      "infovofrobot: False\n",
      "inhandofrobot: False\n",
      "inreachofrobot: False\n",
      "winnie_cabinet_0\n",
      "\n",
      "infovofrobot: False\n",
      "inhandofrobot: False\n",
      "inreachofrobot: False\n",
      "open: False\n",
      "toggled: False\n",
      "{'alligator_busy_box_0': {'infovofrobot': 0, 'inhandofrobot': 0, 'inreachofrobot': 0, 'toggled': 0}, 'ball_0': {'infovofrobot': 0, 'inhandofrobot': 0, 'inreachofrobot': 0}, 'beach_ball_0': {'infovofrobot': 0, 'inhandofrobot': 0, 'inreachofrobot': 0}, 'music_toy_0': {'infovofrobot': 0, 'inhandofrobot': 0, 'inreachofrobot': 0, 'noise': 0, 'toggled': 0}, 'farm_toy_0': {'infovofrobot': 0, 'inhandofrobot': 0, 'inreachofrobot': 0, 'popup': 0, 'toggled': 0}, 'piggie_bank_0': {'infovofrobot': 0, 'inhandofrobot': 0, 'inreachofrobot': 0, 'noise': 0, 'open': 0, 'toggled': 0}, 'rattle_0': {'infovofrobot': 0, 'inhandofrobot': 0, 'inreachofrobot': 0, 'noise': 0}, 'red_spiky_ball_0': {'infovofrobot': 0, 'inhandofrobot': 0, 'inreachofrobot': 0}, 'winnie_cabinet_0': {'infovofrobot': 0, 'inhandofrobot': 0, 'inreachofrobot': 0, 'open': 0, 'toggled': 0}}\n"
     ]
    }
   ],
   "source": [
    "from mini_behavior.utils.states_base import RelativeObjectState\n",
    "print(env.objs)\n",
    "for obj_type in env.objs.values():\n",
    "    for obj in obj_type:\n",
    "        print(obj.name, end = '\\n\\n')\n",
    "        for state_value in obj.states:\n",
    "            if not isinstance(obj.states[state_value], RelativeObjectState):\n",
    "                state = obj.states[state_value].get_value(env)\n",
    "                print(str(state_value) + \": \" + str(state))\n",
    "\n",
    "exploration_dict = {obj.name: {} for obj_type in env.objs.values() for obj in obj_type}\n",
    "for dict, obj in zip(exploration_dict.values(), list(obj for obj_type in env.objs.values() for obj in obj_type)):\n",
    "    for state_value in obj.states:\n",
    "        if not isinstance(obj.states[state_value], RelativeObjectState):\n",
    "            dict[state_value] = 0\n",
    "print(exploration_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = init_env(NUM_ENVS)\n",
    "    \n",
    "print('begin training')\n",
    "# Policy training\n",
    "model = APT_PPO(env, num_envs=NUM_ENVS, total_timesteps = TOTAL_TIMESTEPS, num_steps=NUM_STEPS, save_freq = 10)\n",
    "\n",
    "model.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Define the directory path\n",
    "save_dir = \"models/ATP_PPO_Trial\"\n",
    "\n",
    "# Check if the directory exists, and if not, create it\n",
    "if not os.path.exists(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "model.save(f\"{save_dir}/{env_name}\", env_kwargs= env_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'numpy._core'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m checkpoint \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/Users/kevinhan/mini_behavior/models/APT_PPO_MultiToy_Run3/MiniGrid-MultiToy-16x16-N2-v0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/babyRL/lib/python3.8/site-packages/torch/serialization.py:1026\u001b[0m, in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[1;32m   1024\u001b[0m             \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m   1025\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m pickle\u001b[38;5;241m.\u001b[39mUnpicklingError(UNSAFE_MESSAGE \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1026\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43mopened_zipfile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1027\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1028\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mpickle_module\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1029\u001b[0m \u001b[43m                     \u001b[49m\u001b[43moverall_storage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverall_storage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1030\u001b[0m \u001b[43m                     \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpickle_load_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1031\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mmap:\n\u001b[1;32m   1032\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmmap can only be used with files saved with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1033\u001b[0m                        \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`torch.save(_use_new_zipfile_serialization=True), \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1034\u001b[0m                        \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mplease torch.save your checkpoint with this option in order to use mmap.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/babyRL/lib/python3.8/site-packages/torch/serialization.py:1438\u001b[0m, in \u001b[0;36m_load\u001b[0;34m(zip_file, map_location, pickle_module, pickle_file, overall_storage, **pickle_load_args)\u001b[0m\n\u001b[1;32m   1436\u001b[0m unpickler \u001b[38;5;241m=\u001b[39m UnpicklerWrapper(data_file, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpickle_load_args)\n\u001b[1;32m   1437\u001b[0m unpickler\u001b[38;5;241m.\u001b[39mpersistent_load \u001b[38;5;241m=\u001b[39m persistent_load\n\u001b[0;32m-> 1438\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43munpickler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1440\u001b[0m torch\u001b[38;5;241m.\u001b[39m_utils\u001b[38;5;241m.\u001b[39m_validate_loaded_sparse_tensors()\n\u001b[1;32m   1441\u001b[0m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_log_api_usage_metadata(\n\u001b[1;32m   1442\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch.load.metadata\u001b[39m\u001b[38;5;124m\"\u001b[39m, {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mserialization_id\u001b[39m\u001b[38;5;124m\"\u001b[39m: zip_file\u001b[38;5;241m.\u001b[39mserialization_id()}\n\u001b[1;32m   1443\u001b[0m )\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/babyRL/lib/python3.8/site-packages/torch/serialization.py:1431\u001b[0m, in \u001b[0;36m_load.<locals>.UnpicklerWrapper.find_class\u001b[0;34m(self, mod_name, name)\u001b[0m\n\u001b[1;32m   1429\u001b[0m         \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m   1430\u001b[0m mod_name \u001b[38;5;241m=\u001b[39m load_module_mapping\u001b[38;5;241m.\u001b[39mget(mod_name, mod_name)\n\u001b[0;32m-> 1431\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind_class\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmod_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy._core'"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "checkpoint = torch.load(\n",
    "    \"/Users/kevinhan/mini_behavior/models/APT_PPO_MultiToy_Run3/MiniGrid-MultiToy-16x16-N2-v0\", \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['env_kwargs', 'model_saves', 'final_model_state_dict', 'final_optimizer_state_dict', 'learning_rate', 'total_timesteps', 'num_envs', 'num_steps', 'curiosity_rewards', 'actions', 'observations', 'exploration_metrics', 'exploration_percentages', 'test_actions', 'test_action_heat_map', 'state_exploration_graph_per_obj', 'state_exploration_all_objs'])\n"
     ]
    }
   ],
   "source": [
    "print(checkpoint.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All tensors have the same length.\n",
      "tensor([5, 2, 0, 3, 0, 5, 3, 4, 6, 3, 1, 5, 6, 0, 5, 1, 2, 1, 5, 0, 0, 2, 6, 4,\n",
      "        3, 0, 5, 6, 3, 6, 3, 6, 2, 5, 2, 0, 5, 4, 6, 1, 5, 2, 5, 6, 1, 3, 1, 5,\n",
      "        2, 1, 2, 3, 2, 3, 0, 2, 2, 3, 2, 0, 3, 4, 6, 3, 2, 6, 5, 3, 6, 0, 4, 3,\n",
      "        3, 6, 6, 1, 1, 4, 3, 0, 6, 2, 4, 3, 6, 3, 1, 5, 6, 4, 1, 3, 3, 1, 4, 2,\n",
      "        6, 6, 0, 1, 0, 5, 6, 1, 5, 0, 0, 4, 1, 5, 6, 2, 3, 6, 2, 2, 0, 4, 5, 0,\n",
      "        3, 1, 2, 3, 6, 1, 5, 4, 0, 4, 3, 6, 5, 6, 4, 0, 6, 0, 2, 4, 2, 3, 5, 5,\n",
      "        0, 0, 3, 1, 2, 6, 3, 3, 2, 5, 2, 4, 6, 3, 2, 1, 0, 6, 2, 5, 3, 5, 4, 0,\n",
      "        3, 6, 6, 4, 4, 2, 0, 3, 6, 5, 4, 1, 3, 1, 4, 2, 6, 3, 4, 6, 0, 4, 5, 2,\n",
      "        1, 3, 5, 2, 2, 2, 4, 0, 6, 6, 6, 3, 3, 3, 4, 5, 2, 5, 5, 4, 6, 1, 1, 5,\n",
      "        3, 6, 3, 6, 0, 3, 5, 2, 5, 4, 0, 4, 5, 6, 3, 4, 4, 3, 3, 1, 3, 6, 3, 6,\n",
      "        0, 2, 4, 2, 3, 1, 2, 6, 4, 6, 3, 5, 6, 5, 5, 1, 3, 2, 1, 1, 4, 5, 3, 2,\n",
      "        3, 6, 0, 5, 2, 0, 1, 6, 0, 0, 3, 1, 4, 4, 1, 6, 1, 4, 5, 1, 3, 0, 3, 4,\n",
      "        0, 2, 4, 4, 1, 3, 4, 3, 0, 0, 0, 2, 2, 3, 5, 3, 5, 1, 1, 5, 1, 1, 2, 2,\n",
      "        4, 3, 2, 1, 1, 0, 5, 4, 3, 5, 3, 2, 5, 0, 5, 2, 4, 6, 0, 1, 4, 1, 2, 3,\n",
      "        4, 2, 3, 6, 4, 0, 4, 2, 6, 2, 3, 0, 4, 3, 4, 1, 5, 3, 0, 5, 0, 6, 4, 6,\n",
      "        5, 6, 2, 2, 6, 2, 1, 2, 6, 6, 2, 5, 5, 0, 1, 2, 1, 2, 4, 4, 2, 6, 3, 5,\n",
      "        2, 2, 3, 6, 2, 4, 1, 4, 0, 2, 2, 5, 0, 2, 0, 6, 5, 5, 2, 6, 3, 0, 0, 4,\n",
      "        0, 3, 2, 1, 4, 0, 3, 0, 2, 6, 0, 1, 2, 6, 1, 1, 6, 4, 4, 2, 4, 2, 3, 4,\n",
      "        6, 1, 6, 4, 5, 5, 4, 1, 1, 0, 1, 6, 3, 4, 6, 1, 5, 2, 1, 6, 5, 0, 3, 4,\n",
      "        5, 1, 3, 4, 2, 5, 3, 6, 2, 2, 2, 6, 6, 5, 2, 5, 0, 1, 0, 2, 4, 5, 4, 2,\n",
      "        3, 2, 1, 4, 3, 4, 2, 1, 1, 1, 4, 5, 6, 3, 3, 2, 6, 6, 6, 4, 5, 5, 3, 5,\n",
      "        5, 2, 5, 1, 3, 2, 6, 4, 6, 1, 3, 6, 2, 5, 6, 2, 2, 5, 2, 5, 0, 5, 0, 0,\n",
      "        6, 2, 1, 1, 4, 6, 6, 6, 1, 3, 2, 0, 5, 0, 6, 5, 5, 2, 0, 6, 1, 1, 5, 5,\n",
      "        6, 4, 1, 1, 4, 6, 3, 2, 2, 2, 0, 1, 2, 5, 5, 6, 5, 4, 2, 1, 6, 6, 6, 5,\n",
      "        6, 6, 0, 4, 4, 0, 3, 1, 5, 0, 5, 2, 5, 4, 5, 1, 1, 6, 2, 3, 5, 1, 2, 5,\n",
      "        6, 1, 0, 3, 3, 3, 5, 1, 6, 4, 6, 5, 0, 2, 6, 2, 4, 2, 5, 2, 2, 5, 3, 2,\n",
      "        2, 3, 2, 6, 0, 2, 5, 1, 2, 3, 2, 5, 3, 6, 1, 2, 3, 6, 2, 1, 2, 0, 1, 1,\n",
      "        1, 5, 2, 5, 5, 6, 4, 2, 5, 6, 6, 3, 6, 3, 2, 1, 2, 2, 1, 5, 6, 2, 2, 6,\n",
      "        1, 2, 1, 0, 4, 0, 5, 6, 2, 1, 3, 1, 0, 5, 0, 5, 0, 6, 0, 3, 1, 1, 5, 4,\n",
      "        1, 1, 3, 0, 4, 3, 5, 3, 4, 5, 1, 0, 4, 0, 4, 5, 1, 3, 6, 5, 1, 0, 5, 1,\n",
      "        3, 3, 3, 1, 1, 0, 6, 6, 4, 6, 5, 3, 3, 6, 5, 6, 6, 3, 4, 1, 0, 0, 3, 6,\n",
      "        5, 6, 6, 4, 1, 6, 5, 3, 3, 4, 6, 5, 0, 5, 6, 0, 4, 3, 1, 4, 6, 5, 0, 0,\n",
      "        5, 6, 2, 5, 6, 3, 4, 3, 1, 6, 1, 0, 2, 2, 5, 2, 2, 3, 0, 5, 4, 4, 2, 1,\n",
      "        2, 3, 0, 1, 0, 1, 4, 6, 4, 4, 2, 2, 4, 4, 3, 0, 1, 1, 3, 2, 6, 0, 4, 4,\n",
      "        3, 6, 4, 6, 5, 6, 1, 4, 2, 1, 3, 1, 1, 4, 5, 1, 1, 2, 3, 3, 3, 2, 5, 0,\n",
      "        2, 1, 1, 2, 5, 6, 5, 4, 0, 1, 3, 4, 5, 2, 3, 0, 6, 5, 3, 3, 6, 6, 6, 4,\n",
      "        6, 1, 6, 1, 1, 5, 2, 1, 5, 0, 2, 4, 0, 6, 0, 6, 3, 0, 6, 3, 2, 5, 0, 1,\n",
      "        6, 2, 4, 3, 5, 5, 2, 1, 0, 2, 3, 4, 2, 5, 3, 2, 2, 4, 3, 6, 6, 0, 0, 6,\n",
      "        1, 5, 0, 3, 4, 5, 4, 0, 5, 0, 4, 2, 2, 2, 0, 1, 4, 0, 4, 6, 2, 6, 0, 6,\n",
      "        5, 5, 4, 6, 3, 5, 0, 3, 6, 0, 0, 6, 2, 3, 5, 6, 4, 2, 1, 0, 3, 1, 5, 6,\n",
      "        2, 3, 0, 0, 1, 2, 3, 2, 0, 5, 3, 3, 0, 2, 3, 4, 6, 4, 3, 3, 2, 2, 3, 6,\n",
      "        0, 1, 4, 4, 5, 3, 5, 5, 5, 0, 4, 2, 1, 5, 5, 3])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from IPython.display import display\n",
    "\n",
    "actions = checkpoint['actions']\n",
    "rewards = checkpoint['curiosity_rewards']\n",
    "def all_tensors_same_length(tensor_list):\n",
    "    lengths = [t.size(0) for t in tensor_list]  # Get the length of each tensor\n",
    "    return all(length == lengths[0] for length in lengths)\n",
    "\n",
    "# Usage\n",
    "if all_tensors_same_length(actions):\n",
    "    print(\"All tensors have the same length.\")\n",
    "else:\n",
    "    print(\"Tensors have different lengths.\")\n",
    "actions = [tensor.to(torch.int64).flatten() for tensor in actions]\n",
    "print(actions[2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.) tensor(407)\n",
      "tensor(2.) tensor(591)\n",
      "tensor(5.) tensor(1)\n",
      "tensor(6.) tensor(1)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "unique_numbers, counts = torch.unique(actions[970], return_counts=True)\n",
    "\n",
    "# Print the results\n",
    "for i,j in zip(unique_numbers, counts):\n",
    "    print(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Find the maximum value to determine the heatmap's size\n",
    "\n",
    "max_value = max(int(torch.max(tensor)) for tensor in actions)\n",
    "\n",
    "# Create a frequency matrix where rows are episodes and columns are possible actions\n",
    "heatmap_data = []\n",
    "for tensor in actions:\n",
    "    counts = torch.bincount(tensor, minlength=max_value + 1)\n",
    "    heatmap_data.append(counts.numpy())\n",
    "\n",
    "# Convert to a 2D NumPy array for visualization\n",
    "heatmap_data = np.array(heatmap_data)\n",
    "\n",
    "# Create the heatmap\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.imshow(heatmap_data, aspect='auto', cmap='viridis', interpolation='nearest')\n",
    "plt.colorbar(label='Frequency')\n",
    "plt.xlabel('Action')\n",
    "plt.ylabel('Episode')\n",
    "plt.title('Action Frequencies per Episode')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(rewards, linestyle='-', color='blue', label='Average Reward')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Average Reward')\n",
    "plt.title('Rewards per Episode')\n",
    "plt.legend()\n",
    "\n",
    "# Show grid for better readability\n",
    "plt.grid(True, linestyle='--', alpha=0.6)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "support_cache = supports(armies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'B': 1, 'C': 1, 'D': 1}\n"
     ]
    }
   ],
   "source": [
    "print(support_cache)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.656854249492381\n",
      "5.656854249492381\n"
     ]
    }
   ],
   "source": [
    "from numpy import linalg as LA\n",
    "p1, p2 = np.array([0, 0]), np.array([4, 4])\n",
    "dp = LA.norm((p1 - p2))\n",
    "print(np.sqrt(32))\n",
    "print(dp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one 1\n",
      "two 2\n"
     ]
    }
   ],
   "source": [
    "test_dict = {\"one\": 1, \"two\": 2}\n",
    "\n",
    "for i,j in zip(test_dict.keys(), test_dict.values()):\n",
    "    print(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7xsTE6OLFi/rqq6+yzQ/hztiWpAoVKmRLFJQuXVo//fTTTWP+7rvvNH78eG3evDlbwigpKSlP70mFChXUsWPHHPdx5bPp7rjKaQwEBwe7dI6/2r9/v3766acbtoKdOXNGkvSf//xHS5YsUdeuXXXrrbeqc+fO6tOnj+6+++5cPe81NxvX1z7bf/3sFylSJNudcm7Ebrdr7NixGjt2rBISErR+/XrNmjVLS5YsUdGiRfXee+/d9ByujKcbxVqmTJlsyRxX33cAAP6KBAkAn5KVlSWbzaavvvpK/v7+2baXLFlS0tU5Hjp16qTff/9dTz31lGrXrq0SJUroxIkTGjRokFsT/V3veXJabxiGpKuTDLZp00bBwcF6/vnnVa1aNRUrVkw7duzQU089Zfpkg7mt1riRa78YjRw5UiNHjsy2/dNPP9XgwYNNfc7cutnPQpKmT5+uQYMG6fPPP9fXX3+txx57TDExMdqyZctN52q4kffee0+DBg1SVFSUnnjiCYWGhsrf318xMTGOCTD/rEuXLlq5cqWmTp2qtm3bqlixYo5tro5td17z9Rw8eFAdOnRQ7dq19corr6hixYoKCAjQl19+qRkzZuT7JJhmfjb/LLfvR06ysrLUqVMnPfnkk9fdfi2BGRoaql27dmnVqlX66quv9NVXX2nBggUaMGCAY/6e3MiP15ST8uXLq1+/furVq5fq1q2rJUuWKDY29oZzk0j5M55cfd8BAPgrEiQAfEq1atVkGIYiIyNz/JL8888/69dff9XChQs1YMAAx/rrlcCbnVi4Zt26dUpMTNRnn32m1q1bO9YfPnz4uvufPHlSKSkpTlUkv/76qyTl+NfgypUrKysrS/v373ea9PH06dM6f/68YyJGdxiGoQ8++EDt2rXLVhIvSZMmTdL777+vwYMHq2rVqpKk3bt353hOd97nypUra9++fdnWX2tLys1rkqT69eurfv36evbZZ7Vp0ya1aNFCc+fO1QsvvJCr833yySeqWrWqPvvsM6fXN378+Ovuf+edd+rf//63/v73v6t3795aunSp45dPV8d2Xi1fvlxpaWn64osvnCoUrnfnnfz4bLj62XR1XOWnatWq6eLFizetiJGutjh1795d3bt3V1ZWlv7zn//ozTff1HPPPafq1avny3t57XNw4MABpxamjIwMHTlyJFuLoquKFi2qBg0aaP/+/Y42rxvF7+p4+nOsf66uSkxMzFZF5M77DgDAnzEHCQCf8o9//EP+/v6aOHFitr+iGoahxMRESX/85fXP+xiG4bjt5p9dS0i42m/vquvFcOXKFb3xxhvX3T8jI0Nvvvmm075vvvmmbrnlFjVu3PiGz3PPPfdIUrY7O7zyyiuS5LhDiju+++47HTlyRIMHD9Z9992Xbenbt6/Wrl2rkydP6pZbblHr1q01f/58xcfHO53nz6/dnff5nnvu0datW7V582bHupSUFL311luqUqWKbrvtNrdeT3JysjIyMpzW1a9fX35+fk63QnbX9X7G33//vVPcf9WxY0ctXrxYK1eu1D//+U/HX9hdHdt5db2Yk5KStGDBgmz7lihRokA+F9f7bLo6rvJTnz59tHnzZq1atSrbtvPnzzvG1F9/Nn5+fo7kxLXxlR/XmSZNmqhs2bJ6++23ncb3+++/ny3pcD379+/P9t5ei3Hz5s0qXbq0o83lRvG7Op46dOigIkWKZLv97+uvv57t+V193wEA+CsqSAD4lGrVqumFF15QdHS043aWQUFBOnz4sJYuXaqhQ4dqzJgxql27tqpVq6YxY8boxIkTCg4O1qeffnrdXxquJR8ee+wxdenSRf7+/urXr1+eY23evLlKly6tgQMH6rHHHpPNZtOiRYtu+MtdRESEpkyZoiNHjqhmzZr66KOPtGvXLr311lvZ5p/4s4YNG2rgwIF66623HG09W7du1cKFCxUVFeX0l2VXvf/++/L3979hcuXee+/V2LFjtXjxYo0aNUqvvvqqWrZsqb/97W8aOnSoIiMjdeTIEf33v//Vrl27JP3xPo8dO1b9+vVT0aJF1b1792zzrkjS008/rQ8//FBdu3bVY489pjJlymjhwoU6fPiwPv30U/n5uff3gW+++UaPPvqoevfurZo1ayojI0OLFi2Sv7+/evXq5d6b8yd///vf9dlnn6lnz57q1q2bDh8+rLlz5+q2227TxYsXb3hcVFSUowUjODhYb775pstjO686d+7sqHZ4+OGHdfHiRb399tsKDQ1VQkKC076NGzfWnDlz9MILL6h69eoKDQ1V+/btczz/r7/+et15K8LCwtSpUye3PpuujKv89MQTT+iLL77Q3//+dw0aNEiNGzdWSkqKfv75Z33yySc6cuSIypUrpyFDhuj3339X+/btVaFCBR09elSvvfaaGjVq5KjqatSokfz9/TVlyhQlJSXJbrerffv2Cg0NzXV8AQEBmjBhgoYPH6727durT58+OnLkiGJjY1WtWrWbVq38+OOPuv/++9W1a1e1atVKZcqU0YkTJ7Rw4UKdPHlSM2fOdCRAbvT5dXU8hYWF6fHHH9f06dN177336u6779aPP/6or776SuXKlXOK1dX3HQCAbArobjkAkC+u3eb3hx9+uO72Nm3aON3m95pPP/3UaNmypVGiRAmjRIkSRu3atY1hw4YZ+/btc+zzyy+/GB07djRKlixplCtXznjooYeMH3/80ZBkLFiwwLFfRkaGMXz4cOOWW24xbDab45a/f741659duwXnX28Ze73X8t133xl33nmnERgYaERERBhPPvmksWrVqmy3+7z2Ordt22bcddddRrFixYzKlSsbr7/+ukvvY3p6ujFx4kQjMjLSKFq0qFGxYkUjOjra6VbChuHabX6vXLlilC1b1mjVqlWO+0VGRhq333674/Hu3buNnj17GqVKlTKKFStm1KpVy3juueecjpk0aZJx6623Gn5+fk63DP3rbX4NwzAOHjxo3HfffY7z3XHHHcaKFSuc9rnRz+Laz+7az/nQoUPGgw8+aFSrVs0oVqyYUaZMGaNdu3bG//73vxxf41/99TawWVlZxksvvWRUrlzZsNvtxu23326sWLHCGDhwoFG5cuVs8fx1LL3xxhuGJGPMmDGOda6M7Rt9Lv76vDfyxRdfGA0aNDCKFStmVKlSxZgyZYoxf/78bLdxPXXqlNGtWzcjKCjIpdvGKofb/P75WFc/m4Zx83F17Ta/Z8+edTru2ufxerelvZHr3db4woULRnR0tFG9enUjICDAKFeunNG8eXNj2rRpjttvf/LJJ0bnzp2N0NBQIyAgwKhUqZLx8MMPGwkJCU7nevvtt42qVasa/v7+TteAG93m92bj+ppXX33VMQbvuOMO47vvvjMaN25s3H333Tm+3tOnTxuTJ0822rRpY5QvX94oUqSIUbp0aaN9+/bGJ598km3/G31+XR1PGRkZxnPPPWeEh4cbgYGBRvv27Y24uDijbNmyxr///W+333cAAP7KZhgFVGcKAMg3bdu21W+//WbpfAsAvENWVpZuueUW/eMf/9Dbb79tdTg5On/+vEqXLq0XXnhBY8eOtTocAICHYw4SAAAAH3X58uVsbXvvvvuufv/992y3krbapUuXsq27NndSYYsVAOCZmIMEAADAR23ZskUjR45U7969VbZsWe3YsUPz5s1TvXr11Lt3b6vDc/LRRx8pNjZW99xzj0qWLKmNGzfqww8/VOfOndWiRQurwwMAeAESJAAAAD6qSpUqqlixol599VX9/vvvKlOmjAYMGKDJkycrICDA6vCcNGjQQEWKFNHUqVOVnJzsmLg1t7fZBgDgr5iDBAAAAAAAFIg5c+Zozpw5OnLkiCSpbt26GjdunLp27XrDYz7++GM999xzOnLkiGrUqKEpU6bonnvucWw3DEPjx4/X22+/rfPnz6tFixaaM2eOatSo4VZszEECAAAAAAAKRIUKFTR58mRt375d27ZtU/v27dWjRw/t2bPnuvtv2rRJ/fv317/+9S/t3LlTUVFRioqKcro5wdSpU/Xqq69q7ty5+v7771WiRAl16dJFly9fdis2KkgAAAAAAIBlypQpo5dffln/+te/sm3r27evUlJStGLFCse6O++8U40aNdLcuXNlGIYiIiI0evRojRkzRpKUlJSksLAwxcbGql+/fi7HQQUJAAAAAADItbS0NCUnJzstaWlpNz0uMzNTixcvVkpKiu66667r7rN582Z17NjRaV2XLl20efNmSdLhw4d16tQpp31CQkLUrFkzxz6u8spJWi9nWB0BYK7STR+1OgTANA+OG2Z1CACA65jevY7VIQCmKuaVv+3+IfD2wvM7wlM9ymnixIlO68aPH68JEyZcd/+ff/5Zd911ly5fvqySJUtq6dKluu22266776lTpxQWFua0LiwsTKdOnXJsv7buRvu4ysuHDAAAAAAAyE/R0dEaNWqU0zq73X7D/WvVqqVdu3YpKSlJn3zyiQYOHKj169ffMElSUEiQAAAAAACAXLPb7TkmRP4qICBA1atXlyQ1btxYP/zwg2bNmqU333wz277h4eE6ffq007rTp08rPDzcsf3auvLlyzvt06hRI7deB3OQAAAAAADgaWx+hWfJo6ysrBvOWXLXXXdpzZo1TutWr17tmLMkMjJS4eHhTvskJyfr+++/v+G8JjdCBQkAAAAAACgQ0dHR6tq1qypVqqQLFy7ogw8+0Lp167Rq1SpJ0oABA3TrrbcqJiZGkvT444+rTZs2mj59urp166bFixdr27ZteuuttyRJNptNI0aM0AsvvKAaNWooMjJSzz33nCIiIhQVFeVWbCRIAAAAAABAgThz5owGDBighIQEhYSEqEGDBlq1apU6deokSYqPj5ef3x9VKc2bN9cHH3ygZ599Vs8884xq1KihZcuWqV69eo59nnzySaWkpGjo0KE6f/68WrZsqZUrV6pYsWJuxWYzDMMw52UWHtzFBt6Gu9jAm3A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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_actions = [\n",
    "    ['move_left', 'move_right', 'jump', 'duck', 'move_left', 'jump', 'shoot', 'jump', 'move_right'],  # Stage 1\n",
    "    ['move_right', 'duck', 'jump', 'jump', 'move_left', 'shoot', 'duck', 'move_right', 'shoot'],      # Stage 2\n",
    "    ['move_left', 'move_right', 'shoot', 'duck', 'duck', 'jump', 'move_left', 'jump', 'shoot'],      # Stage 3\n",
    "    ['jump', 'jump', 'move_left', 'move_right', 'shoot', 'shoot', 'duck', 'duck', 'jump'],           # Stage 4\n",
    "    ['move_left', 'move_right', 'shoot', 'duck', 'jump', 'move_right', 'move_left', 'duck', 'shoot'],# Stage 5\n",
    "    ['duck', 'move_right', 'move_right', 'jump', 'shoot', 'move_left', 'jump', 'shoot', 'duck'],     # Stage 6\n",
    "    ['jump', 'move_left', 'shoot', 'duck', 'jump', 'move_right', 'duck', 'move_right', 'shoot'],     # Stage 7\n",
    "    ['move_left', 'duck', 'jump', 'move_right', 'shoot', 'jump', 'move_left', 'shoot', 'duck'],      # Stage 8\n",
    "    ['shoot', 'move_right', 'duck', 'move_left', 'jump', 'shoot', 'duck', 'move_left', 'jump'],      # Stage 9\n",
    "    ['jump', 'shoot', 'duck', 'move_left', 'move_right', 'move_left', 'jump', 'shoot', 'duck']       # Stage 10\n",
    "]\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# Create a DataFrame of action counts per stage\n",
    "unique_actions = sorted(set(action for actions in test_actions for action in actions))\n",
    "data = pd.DataFrame(0, index=range(1, len(test_actions) + 1), columns=unique_actions)\n",
    "\n",
    "# Populate the DataFrame with counts\n",
    "for i, actions in enumerate(test_actions):\n",
    "    for action in actions:\n",
    "        data.loc[i + 1, action] += 1\n",
    "\n",
    "# Plot the heatmap\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.heatmap(data, annot=True, cmap=\"Blues\", fmt='d', ax=ax)\n",
    "\n",
    "# Customize the plot\n",
    "ax.set_xlabel('Actions')\n",
    "ax.set_ylabel('Testing Stages')\n",
    "ax.set_title('Heatmap of Actions Taken at Each Testing Stage')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "babyRL",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}