{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Solving Brax Problems in EvoX\n", "\n", "EvoX deeply dives into neuroevolution with Brax.\n", "Here we will show an example of solving Brax problem in EvoX." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# install EvoX and Brax, skip it if you have already installed EvoX or Brax\n", "from importlib.util import find_spec\n", "from IPython.display import HTML\n", "\n", "if find_spec(\"evox\") is None:\n", " %pip install evox\n", "if find_spec(\"brax\") is None:\n", " %pip install brax" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# The dependent packages or functions in this example\n", "import torch\n", "import torch.nn as nn\n", "\n", "from evox.algorithms import PSO\n", "from evox.problems.neuroevolution.brax import BraxProblem\n", "from evox.utils import ParamsAndVector\n", "from evox.workflows import EvalMonitor, StdWorkflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is Brax\n", "\n", "Brax is a fast and fully differentiable physics engine used for research and development of robotics, human perception, materials science, reinforcement learning, and other simulation-heavy applications. \n", "\n", "Here we will demonstrate a \"swimmer\" environment of Brax. \n", "\n", "For more information, you can browse the [Github of Brax](https://github.com/google/brax)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Design a neural network class\n", "\n", "To start with, we need to decide which neural network we are about to construct.\n", "\n", "Here we will give a simple Multilayer Perceptron (MLP) class. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Construct an MLP using PyTorch.\n", "# This MLP has 3 layers.\n", "\n", "\n", "class SimpleMLP(nn.Module):\n", " def __init__(self):\n", " super(SimpleMLP, self).__init__()\n", " self.features = nn.Sequential(nn.Linear(17, 8), nn.Tanh(), nn.Linear(8, 6))\n", "\n", " def forward(self, x):\n", " x = self.features(x)\n", " return torch.tanh(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initiate a model\n", "\n", "Through the ``SimpleMLP`` class, we can initiate a MLP model." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Make sure that the model is on the same device, better to be on the GPU\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "# Reset the random seed\n", "seed = 1234\n", "torch.manual_seed(seed)\n", "torch.cuda.manual_seed_all(seed)\n", "\n", "# Initialize the MLP model\n", "model = SimpleMLP().to(device)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initiate an adapter\n", "\n", "An adapter can help us convert the data back-and-forth." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "adapter = ParamsAndVector(dummy_model=model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With an adapter, we can set out to do this Neuroevolution Task.\n", "\n", "## Set up the running process\n", "\n", "### Initiate an algorithm and a problem\n", "\n", "We initiate a [PSO algorithm](#evox.algorithms.so.pso_variants.pso.PSO), and the problem is a [Brax problem](#evox.problems.neuroevolution.brax.BraxProblem) in \"swimmer\" environment." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Set the population size\n", "POP_SIZE = 1024\n", "\n", "# Get the bound of the PSO algorithm\n", "model_params = dict(model.named_parameters())\n", "pop_center = adapter.to_vector(model_params)\n", "lower_bound = torch.full_like(pop_center, -5)\n", "upper_bound = torch.full_like(pop_center, 5)\n", "\n", "# Initialize the PSO, and you can also use any other algorithms\n", "algorithm = PSO(\n", " pop_size=POP_SIZE,\n", " lb=lower_bound,\n", " ub=upper_bound,\n", " device=device,\n", ")\n", "\n", "# Initialize the Brax problem\n", "problem = BraxProblem(\n", " policy=model,\n", " env_name=\"halfcheetah\",\n", " max_episode_length=1000,\n", " num_episodes=3,\n", " pop_size=POP_SIZE,\n", " device=device,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, we will be using 1000 steps for each episode, and the average reward of 3 episodes will be returned as the fitness value.\n", "\n", "### Set an monitor" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# set an monitor, and it can record the top 3 best fitnesses\n", "monitor = EvalMonitor(\n", " topk=3,\n", " device=device,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initiate an workflow" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Initiate an workflow\n", "workflow = StdWorkflow(\n", " algorithm=algorithm,\n", " problem=problem,\n", " monitor=monitor,\n", " opt_direction=\"max\",\n", " solution_transform=adapter,\n", " device=device,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run the workflow\n", "\n", "Run the workflow and see the magic!\n", "\n", "```{note}\n", "The following block will take around 20 minute to run.\n", "The time may vary depending on your hardware.\n", "```" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generation 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "W0223 23:03:30.918000 260895 torch/_dynamo/convert_frame.py:906] [7/8] torch._dynamo hit config.cache_size_limit (8)\n", "W0223 23:03:30.918000 260895 torch/_dynamo/convert_frame.py:906] [7/8] function: 'torch_dynamo_resume_in__evaluate_at_109' (/home/bchuang/evox/src/evox/workflows/std_workflow.py:109)\n", "W0223 23:03:30.918000 260895 torch/_dynamo/convert_frame.py:906] [7/8] last reason: 7/0: len(L['self']._modules['monitor'].fitness_history) == 1 \n", "W0223 23:03:30.918000 260895 torch/_dynamo/convert_frame.py:906] [7/8] To log all recompilation reasons, use TORCH_LOGS=\"recompiles\".\n", "W0223 23:03:30.918000 260895 torch/_dynamo/convert_frame.py:906] [7/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 10\n", "Generation 20\n", "Generation 30\n", "Generation 40\n", "Top fitness: 4250.0322265625\n", "Best params: {'features.0.weight': tensor([[-3.3347, 2.9721, -0.0254, 3.0749, 1.2332, 3.6499, -0.7560, 5.0000,\n", " 1.5111, 2.1191, -3.5206, -4.9118, 3.7702, 0.6951, -3.0276, 4.1081,\n", " -3.2657],\n", " [ 3.1131, 0.4432, -4.5047, -1.6796, 4.1246, -0.7371, 3.6206, -0.5316,\n", " -5.0000, 4.1015, 2.0267, -0.4714, 1.1236, -4.4360, 1.5476, -3.5875,\n", " 1.6689],\n", " [-0.5304, 4.5822, 3.8289, -3.9503, -5.0000, -1.3703, -2.5397, 1.4839,\n", " -1.3603, 0.6347, -1.0380, 2.8088, -4.9362, 1.8826, -0.1080, 2.0410,\n", " -4.0449],\n", " [ 0.3426, 1.6299, 4.1005, -5.0000, 0.2213, -0.0782, -2.5053, -1.2162,\n", " 0.0267, -3.2784, -1.2221, 2.3266, 0.0275, 0.6066, 3.6144, -2.6463,\n", " 1.4504],\n", " [-1.8489, -1.2312, -2.9940, -1.6940, -3.2203, 2.9359, 3.2070, 0.3895,\n", " 1.1279, -0.7632, 2.6870, 2.9413, -4.5347, -5.0000, -4.0150, -2.2413,\n", " 3.6701],\n", " [-1.5935, -4.6028, 0.5348, 4.3558, -2.7510, 5.0000, 2.8905, 1.5238,\n", " -0.3118, 1.1121, 2.3084, -4.8858, -3.8107, -2.1977, 1.4186, -2.9146,\n", " -2.4085],\n", " [-1.7738, 2.5469, -0.5261, -0.3609, 5.0000, 0.1073, 1.2496, 3.2413,\n", " 1.3704, 1.0573, -1.6874, -3.0503, -3.4587, 1.3785, 3.7430, -4.0845,\n", " 3.9828],\n", " [ 1.8548, 2.0673, -4.7278, 0.7886, 1.6988, 0.4780, -0.5600, -4.8201,\n", " 1.2267, 2.0275, 1.4830, 2.0981, -3.7445, 3.4802, -4.6520, -2.5367,\n", " 1.0738]], device='cuda:0'), 'features.0.bias': tensor([ 2.3727, 0.1259, -3.4709, -4.1896, 3.8652, -4.8321, -2.8869, -4.6169],\n", " device='cuda:0'), 'features.2.weight': tensor([[-0.9303, -0.2943, -4.5685, 1.6857, -1.1658, 3.4732, 3.3395, -2.9619],\n", " [-1.1965, -4.4495, -0.7448, -2.9372, -2.5817, 1.8018, 3.0451, 3.2452],\n", " [-3.1384, -2.6145, 4.8010, 2.5020, -4.8774, 1.2877, 3.9326, 3.8330],\n", " [-0.1435, 0.7449, 1.0022, -1.6307, -0.9984, -0.5575, -4.5282, -1.6870],\n", " [-4.5211, 3.7859, -0.2851, 3.4758, -2.7132, -2.7944, -0.3109, -3.7477],\n", " [-1.1823, 1.9869, -2.0129, -0.6210, -1.3645, -1.8991, -1.0359, -3.7746]],\n", " device='cuda:0'), 'features.2.bias': tensor([-4.2923, -4.8365, -5.0000, 0.8222, 2.4820, -2.2493], device='cuda:0')}\n" ] } ], "source": [ "# Set the maximum number of generations\n", "max_generation = 50\n", "\n", "# Run the workflow\n", "workflow.init_step()\n", "compiled_step = torch.compile(workflow.step)\n", "for i in range(max_generation):\n", " if i % 10 == 0:\n", " print(f\"Generation {i}\")\n", " compiled_step()\n", "\n", "print(f\"Top fitness: {monitor.get_best_fitness()}\")\n", "best_params = adapter.to_params(monitor.get_best_solution())\n", "print(f\"Best params: {best_params}\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor(4250.0322, device='cuda:0')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monitor.get_best_fitness()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "monitor.plot()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/bchuang/evox/.venv/lib/python3.13/site-packages/torch/_inductor/compile_fx.py:194: UserWarning:\n", "\n", "TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.\n", "\n", "/home/bchuang/evox/.venv/lib/python3.13/site-packages/IPython/core/display.py:475: UserWarning:\n", "\n", "Consider using IPython.display.IFrame instead\n", "\n" ] }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "html_string = problem.visualize(best_params)\n", "escaped_string = html_string.replace('\"', \""\")\n", "HTML(f'')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{important}\n", "- Normally, you only need `HTML(problem.visualize(best_params))` to render. The code above is a workaround to ensure the result is displayed correctly on our website.\n", "- The PSO algorithm is not specifically optimized for this type of task, so performance limitations are expected. This example is for demonstration purposes.\n", "```\n", "\n", "We hope you enjoy solving Brax problems with EvoX and have fun!\n" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.13.2" } }, "nbformat": 4, "nbformat_minor": 4 }