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radekosmulski/dqn_breakout.ipynb

Created May 26, 2024 05:35
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dqn_breakout.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "c6a07039-1109-49f7-a031-7b723ecfdffb",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/radek/miniforge3/envs/cleanrl/lib/python3.10/site-packages/aim/storage/structured/sql_engine/models.py:16: MovedIn20Warning: The ``declarative_base()`` function is now available as sqlalchemy.orm.declarative_base(). (deprecated since: 2.0) (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9)\n",
" Base = declarative_base()\n"
]
}
],
"source": [
"import os\n",
"\n",
"os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
"\n",
"import os\n",
"import random\n",
"import time\n",
"from dataclasses import dataclass\n",
"from pathlib import Path\n",
"from pdb import set_trace\n",
"\n",
"import gymnasium as gym\n",
"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",
"from torch.optim.lr_scheduler import LinearLR\n",
"import tyro\n",
"from stable_baselines3.common.atari_wrappers import (\n",
" ClipRewardEnv,\n",
" EpisodicLifeEnv,\n",
" FireResetEnv,\n",
" MaxAndSkipEnv,\n",
" NoopResetEnv,\n",
")\n",
"from stable_baselines3.common.buffers import ReplayBuffer\n",
"from torch.utils.tensorboard import SummaryWriter\n",
"\n",
"import stable_baselines3 as sb3\n",
"\n",
"from aim import Run\n",
"dry_run = False"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c4357881-089d-4942-8f02-546c3f5e1f52",
"metadata": {},
"outputs": [],
"source": [
"@dataclass\n",
"class Args:\n",
" seed: int = 42\n",
" # Algorithm specific arguments\n",
" env_id: str = \"BreakoutNoFrameskip-v4\"\n",
" \"\"\"the id of the environment\"\"\"\n",
" total_timesteps: int = 10000000\n",
" \"\"\"total timesteps of the experiments\"\"\"\n",
" learning_rate: float = 1e-4\n",
" \"\"\"the learning rate of the optimizer\"\"\"\n",
" num_envs: int = 1\n",
" \"\"\"the number of parallel game environments\"\"\"\n",
" buffer_size: int = 1000000\n",
" \"\"\"the replay memory buffer size\"\"\"\n",
" gamma: float = 0.99\n",
" \"\"\"the discount factor gamma\"\"\"\n",
" tau: float = 1.0\n",
" \"\"\"the target network update rate\"\"\"\n",
" target_network_frequency: int = 1000\n",
" \"\"\"the timesteps it takes to update the target network\"\"\"\n",
" batch_size: int = 32\n",
" \"\"\"the batch size of sample from the reply memory\"\"\"\n",
" start_e: float = 1\n",
" \"\"\"the starting epsilon for exploration\"\"\"\n",
" end_e: float = 0.01\n",
" \"\"\"the ending epsilon for exploration\"\"\"\n",
" exploration_fraction: float = 0.10\n",
" \"\"\"the fraction of `total-timesteps` it takes from start-e to go end-e\"\"\"\n",
" learning_starts: int = 80000\n",
" \"\"\"timestep to start learning\"\"\"\n",
" train_frequency: int = 4\n",
" \"\"\"the frequency of training\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "de1282d6-8587-4b2f-9b90-964438900e1a",
"metadata": {},
"outputs": [],
"source": [
"args = Args()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "88c2ca1b-b893-450e-bb5e-70d2b1ca55dd",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"A.L.E: Arcade Learning Environment (version 0.8.1+53f58b7)\n",
"[Powered by Stella]\n"
]
}
],
"source": [
"def make_env():\n",
" env = gym.make(args.env_id, render_mode='rgb_array')\n",
" \n",
" env = gym.wrappers.RecordEpisodeStatistics(env)\n",
" env = NoopResetEnv(env, noop_max=30)\n",
" env = MaxAndSkipEnv(env, skip=4)\n",
" env = EpisodicLifeEnv(env)\n",
" if \"FIRE\" in env.unwrapped.get_action_meanings():\n",
" env = FireResetEnv(env)\n",
" env = ClipRewardEnv(env)\n",
" env = gym.wrappers.ResizeObservation(env, (84, 84))\n",
" env = gym.wrappers.GrayScaleObservation(env)\n",
" env = gym.wrappers.FrameStack(env, 4)\n",
" return env\n",
"\n",
"env = make_env()\n",
"obss = []\n",
"obs, info = env.reset()\n",
"obss.append(obs)\n",
"\n",
"for i in range(400):\n",
" obs, _, terminated, truncated, _ = env.step(env.action_space.sample())\n",
" obss.append(obs)"
]
},
{
"cell_type": "markdown",
"id": "fc66d23e-2603-467a-ab71-2fa7f21314b6",
"metadata": {},
"source": [
"Actions:\n",
"```\n",
"0: NOOP\n",
"1: FIRE\n",
"2: RIGHT\n",
"3: LEFT\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "17c24110-890d-4a35-8e39-f29873a199f1",
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"from matplotlib import animation\n",
"from IPython.display import HTML\n",
"\n",
"def animate_list_of_of_images(video):\n",
" video = np.array(video)\n",
" \n",
" fig = plt.figure()\n",
" im = plt.imshow(video[0])\n",
" \n",
" plt.close() # this is required to not display the generated image\n",
" \n",
" def init():\n",
" im.set_data(video[0])\n",
" \n",
" def animate(i):\n",
" im.set_data(video[i])\n",
" return im\n",
" \n",
" anim = animation.FuncAnimation(fig, animate, init_func=init, frames=video.shape[0],\n",
" interval=50)\n",
" return HTML(anim.to_html5_video())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "02408849-80e9-4b4e-8de1-0aec0ddf48a8",
"metadata": {},
"outputs": [],
"source": [
"def unstack(obss): return [obs[-1] for obs in obss]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "02469459-4515-4597-9149-f0a42bd82c57",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/radek/miniforge3/envs/cleanrl/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
" warnings.warn(\n"
]
},
{
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"\">\n",
" Your browser does not support the video tag.\n",
"</video>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"animate_list_of_of_images(unstack(obss))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2cdb5f91-fb40-446b-979b-f817829b2856",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/radek/miniforge3/envs/cleanrl/lib/python3.10/site-packages/pkg_resources/__init__.py:121: DeprecationWarning: pkg_resources is deprecated as an API\n",
" warnings.warn(\"pkg_resources is deprecated as an API\", DeprecationWarning)\n",
"/home/radek/miniforge3/envs/cleanrl/lib/python3.10/site-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.\n",
"Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
" declare_namespace(pkg)\n",
"/home/radek/miniforge3/envs/cleanrl/lib/python3.10/site-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('mpl_toolkits')`.\n",
"Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
" declare_namespace(pkg)\n",
"WARNING:imageio_ffmpeg:IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=16, resizing from (84, 84) to (96, 96) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).\n",
"[swscaler @ 0x5842000] Warning: data is not aligned! This can lead to a speed loss\n"
]
},
{
"data": {
"text/html": [
"<video src=\"output.mp4\" controls width=\"256\" height=\"256\">\n",
" Your browser does not support the <code>video</code> element.\n",
" </video>"
],
"text/plain": [
"<IPython.core.display.Video object>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"!rm -rf output.mp4\n",
"\n",
"import imageio\n",
"from IPython.display import Video\n",
"\n",
"imageio.mimsave('output.mp4', unstack(obss))\n",
"video = Video('output.mp4', width=256, height=256)\n",
"video"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a58cde03-b092-46d3-92b8-0642041d4e90",
"metadata": {},
"outputs": [],
"source": [
"class QNetwork(nn.Module):\n",
" def __init__(self, num_outputs):\n",
" super().__init__()\n",
" self.network = nn.Sequential(\n",
" nn.Conv2d(4, 32, 8, stride=4),\n",
" nn.ReLU(),\n",
" nn.Conv2d(32, 64, 4, stride=2),\n",
" nn.ReLU(),\n",
" nn.Conv2d(64, 64, 3, stride=1),\n",
" nn.ReLU(),\n",
" nn.Flatten(),\n",
" nn.Linear(3136, 512),\n",
" nn.ReLU(),\n",
" nn.Linear(512, num_outputs),\n",
" )\n",
"\n",
" def forward(self, x):\n",
" return self.network(x / 255.0)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "140170a9-6d91-47a0-a610-a56b7ed8ee16",
"metadata": {},
"outputs": [],
"source": [
"def linear_schedule(start_e: float, end_e: float, duration: int, t: int):\n",
" slope = (end_e - start_e) / duration\n",
" return max(slope * t + start_e, end_e)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "85a566d8-a976-4b6d-9ef9-a4b3de075d6e",
"metadata": {},
"outputs": [],
"source": [
"def run_rollout_validation(save_recording=True, rec_name=None):\n",
" q_network.eval()\n",
" renders, rewards = [], []\n",
"\n",
" env = make_env()\n",
" obs, info = env.reset(seed=42)\n",
" renders.append(env.render())\n",
" while True:\n",
" with torch.inference_mode():\n",
" if np.random.rand() < 0.05:\n",
" action = env.action_space.sample()\n",
" else:\n",
" q_values = q_network(torch.Tensor(np.array(obs)).unsqueeze(0).to(device))\n",
" action = np.argmax(q_values.cpu().detach().numpy()[0])\n",
" \n",
" obs, reward, termination, _, info = env.step(action)\n",
" rewards.append(reward)\n",
" renders.append(env.render())\n",
" if info['lives'] == 0: break\n",
" if termination: obs, info = env.reset(seed=42)\n",
"\n",
" if save_recording:\n",
" if rec_name is None: rec_name = f'{run.hash}_{sum(rewards)}'\n",
" imageio.mimsave(f'videos/{rec_name}.gif', np.stack(renders))\n",
" \n",
" q_network.train()\n",
" return rewards"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a7d2e8f2-67fd-43ce-9257-77112cc5dfca",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'5b88fc0f421c4f7fbd19650e'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"args = Args()\n",
"\n",
"run = Run(experiment=\"dqn_breakout\", repo='dry_run' if dry_run else None)\n",
"run[\"hparams\"] = vars(args)\n",
"\n",
"!mkdir -p videos\n",
"model_path = Path(f\"models/{run.hash}\")\n",
"!mkdir {model_path} \n",
"\n",
"run.hash"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "960e218c-c376-45c5-a301-08a1f7172f18",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = torch.device(\"cuda\")\n",
"\n",
"q_network = QNetwork(num_outputs=4).to(device)\n",
"optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate)\n",
"scheduler = LinearLR(optimizer, start_factor=1, end_factor=0.01, total_iters=(args.total_timesteps-args.learning_starts))\n",
"\n",
"\n",
"target_network = QNetwork(num_outputs=4).to(device)\n",
"target_network.load_state_dict(q_network.state_dict())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b9e43f26-db4c-4b8a-a0f8-6ebc5a4bc051",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Num trainable params: 1.68618M\n"
]
}
],
"source": [
"print(f'Num trainable params: {sum(param.numel() for param in q_network.parameters()) / 10**6}M')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b4852dca-2121-43b2-ac09-889d00b114fd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1000000"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"args.buffer_size"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "2ed2ac93-cc4a-4074-a585-63cdd76bd899",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"args.train_frequency"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "19e0f9b2-bc26-4b0a-a1c1-3f847019fccb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10000000"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"args.total_timesteps"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "80b75d3f-18c0-4afd-bcaa-22da433ac308",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 0.01, 0.1)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"args.start_e, args.end_e, args.exploration_fraction"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "e9e6ca77-74a1-4f8f-9e49-2bebb940856d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 80003:\tEMA Loss = 0.0000\tEMA qvalues = 0.0000\r"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/radek/miniforge3/envs/cleanrl/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
" return F.conv2d(input, weight, bias, self.stride,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 99999:\tEMA Loss = 0.0086\tEMA qvalues = 0.2105\r"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/radek/miniforge3/envs/cleanrl/lib/python3.10/site-packages/gymnasium/utils/passive_env_checker.py:364: UserWarning: \u001b[33mWARN: No render fps was declared in the environment (env.metadata['render_fps'] is None or not defined), rendering may occur at inconsistent fps.\u001b[0m\n",
" logger.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 9h 49min 32s, sys: 24min 44s, total: 10h 14min 16s\n",
"Wall time: 5h 32min 22s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"rb = ReplayBuffer(\n",
" args.buffer_size, # 1_000_000\n",
" env.observation_space,\n",
" env.action_space,\n",
" device,\n",
" optimize_memory_usage=True,\n",
" handle_timeout_termination=False,\n",
")\n",
"\n",
"alpha = 0.1 # Smoothing factor\n",
"ema_loss = 0.0 # Initialize the Exponential Moving Average loss\n",
"ema_qvalues = 0.0 # Same as above but for qvalues\n",
"\n",
"obs, _ = env.reset(seed=args.seed)\n",
"\n",
"for global_step in range(args.total_timesteps):\n",
" epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)\n",
" if random.random() < epsilon:\n",
" action = env.action_space.sample()\n",
" else:\n",
" q_values = q_network(torch.Tensor(np.array(obs)).unsqueeze(0).to(device))\n",
" action = np.argmax(q_values.cpu().detach().numpy()[0])\n",
"\n",
" next_obs, reward, termination, _, info = env.step(action)\n",
" ####\n",
" # obs: observation at t\n",
" # next_obs: observation at t+1\n",
" # action: action taken at t, either randomly sampled or on policy\n",
" # reward: reward for action\n",
" # termination: whether next_obs is the final observation\n",
" rb.add(obs, next_obs, action, reward, termination, info)\n",
" if termination: obs, _ = env.reset()\n",
"\n",
" # TRY NOT TO MODIFY: CRUCIAL step easy to overlook\n",
" obs = next_obs\n",
"\n",
" if global_step > args.learning_starts:\n",
" if global_step % args.train_frequency == 0:\n",
" data = rb.sample(args.batch_size)\n",
" with torch.no_grad():\n",
" target_max, _ = target_network(data.next_observations).max(dim=1)\n",
" td_target = data.rewards.flatten() + args.gamma * target_max * (1 - data.dones.flatten())\n",
" old_val = q_network(data.observations).gather(1, data.actions).squeeze()\n",
" loss = F.mse_loss(td_target, old_val)\n",
"\n",
" # optimize the model\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
" scheduler.step()\n",
"\n",
" ema_loss = alpha * loss.item() + (1 - alpha) * ema_loss\n",
" ema_qvalues = alpha * old_val.mean().item() + (1 - alpha) * ema_qvalues # maybe best to track just the max value?\n",
" # also, this takes the sum across all exmaples\n",
" # in a batch!!! that is not right\n",
" run.track(loss.item(), name='train loss', step=global_step)\n",
" run.track(old_val.mean().item(), name='qvalues (mean)', step=global_step)\n",
" run.track(old_val.max().item(), name='qvalues (max)', step=global_step)\n",
" run.track(ema_loss, name='ema loss', step=global_step)\n",
" run.track(ema_qvalues, name='ema qvalues (mean)', step=global_step)\n",
"\n",
" if global_step % 100_000 == 0 or global_step-1 == args.total_timesteps:\n",
" rewards = run_rollout_validation(save_recording=True, rec_name=None)\n",
" run.track(sum(rewards), step=global_step, name='rollout reward')\n",
" torch.save(q_network.state_dict(), model_path / f'{global_step}_{sum(rewards)}')\n",
" # print(f'Rollout reward at step {global_step}: {sum(rewards)}')\n",
" \n",
" # update target network\n",
" if global_step % args.target_network_frequency == 0:\n",
" for target_network_param, q_network_param in zip(target_network.parameters(), q_network.parameters()):\n",
" target_network_param.data.copy_(\n",
" args.tau * q_network_param.data + (1.0 - args.tau) * target_network_param.data\n",
" )\n",
" print(f\"Step {global_step}:\\tEMA Loss = {ema_loss:.4f}\\tEMA qvalues = {ema_qvalues:.4f}\", end='\\r')\n",
"\n",
"run.close()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "731e044d-c562-4d34-92ef-1db894202e6c",
"metadata": {},
"outputs": [],
"source": [
"# q_network.load_state_dict(torch.load('models/b94d16616faa40a7b667a04f'))\n",
"# q_network.eval()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "417c821f-f27a-4d51-ad64-ebd0f28d58d2",
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Image\n",
"\n",
"Image('videos/b94d16616faa40a7b667a04f_91.0.gif')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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