examples/06_restart_es.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 06 - Restart Wrappers & CMA-ES Variants\n",
    "### [Last Update: March 2022][![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RobertTLange/evosax/blob/main/examples/06_restart_es.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "!pip install -q git+https://github.com/RobertTLange/evosax.git@main"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Restart Open-ES on CartPole when Fitness \"Converges\"\n",
    "### Double Population Size Each Time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ParameterReshaper: 1282 parameters detected for optimization.\n"
     ]
    }
   ],
   "source": [
    "import jax\n",
    "import jax.numpy as jnp\n",
    "\n",
    "from evosax import OpenES, ParameterReshaper, FitnessShaper, NetworkMapper\n",
    "from evosax.utils import ESLog\n",
    "from evosax.problems import GymnaxFitness\n",
    "\n",
    "rng = jax.random.PRNGKey(0)\n",
    "network = NetworkMapper[\"MLP\"](\n",
    "    num_hidden_units=32,\n",
    "    num_hidden_layers=2,\n",
    "    num_output_units=2,\n",
    "    hidden_activation=\"relu\",\n",
    "    output_activation=\"categorical\",\n",
    ")\n",
    "pholder = jnp.zeros((4,))\n",
    "params = network.init(\n",
    "    rng,\n",
    "    x=pholder,\n",
    "    rng=rng,\n",
    ")\n",
    "\n",
    "param_reshaper = ParameterReshaper(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluator = GymnaxFitness(\"CartPole-v1\", num_env_steps=200, num_rollouts=16)\n",
    "evaluator.set_apply_fn(network.apply)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapperParams(strategy_params=EvoParams(opt_params=OptParams(lrate_init=0.05, lrate_decay=1.0, lrate_limit=0.001, momentum=None, beta_1=0.99, beta_2=0.999, beta_3=None, eps=1e-08, max_speed=None), sigma_init=0.03, sigma_decay=1.0, sigma_limit=0.01, init_min=0.0, init_max=0.0, clip_min=-3.4028235e+38, clip_max=3.4028235e+38), restart_params=RestartParams(min_num_gens=50, min_fitness_spread=1e-12, popsize_multiplier=2, copy_mean=False))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from evosax.restarts import IPOP_Restarter\n",
    "\n",
    "# Define a termination criterion (kwargs - fitness, state, params)\n",
    "def std_criterion(fitness, state, params):\n",
    "    \"\"\"Restart strategy if fitness std across population is small.\"\"\"\n",
    "    # Make sure that you don't use centered ranks/z-scoring here :)\n",
    "    return fitness.std() < 2\n",
    "\n",
    "base_strategy = OpenES(popsize=100, num_dims=param_reshaper.total_params)\n",
    "\n",
    "strategy = IPOP_Restarter(base_strategy, stop_criteria=[std_criterion])\n",
    "es_params = strategy.default_params\n",
    "es_params  # popsize_multiplier - controls increase at each restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapperParams(strategy_params=EvoParams(opt_params=OptParams(lrate_init=0.05, lrate_decay=1.0, lrate_limit=0.001, momentum=None, beta_1=0.99, beta_2=0.999, beta_3=None, eps=1e-08, max_speed=None), sigma_init=0.03, sigma_decay=1.0, sigma_limit=0.01, init_min=0.0, init_max=0.0, clip_min=-3.4028235e+38, clip_max=3.4028235e+38), restart_params=RestartParams(min_num_gens=50, min_fitness_spread=1e-12, popsize_multiplier=2, copy_mean=True))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set option to copy over mean between resets - for OpenES only resets sigma!\n",
    "es_params = es_params.replace(restart_params=es_params.restart_params.replace(copy_mean=True))\n",
    "es_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/anaconda3/envs/mle-toolbox/lib/python3.9/site-packages/flax/core/scope.py:740: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.\n",
      "  abs_value_flat = jax.tree_leaves(abs_value)\n",
      "/Users/rob/anaconda3/envs/mle-toolbox/lib/python3.9/site-packages/flax/core/scope.py:741: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.\n",
      "  value_flat = jax.tree_leaves(value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  0 Perf (Best):  23.25 Perf (Mean):  21.77375 0.7692213\n",
      "Generation:  20 Perf (Best):  68.0 Perf (Mean):  59.039997 3.990374\n",
      "Generation:  40 Perf (Best):  197.25 Perf (Mean):  185.15125 9.462757\n",
      "--> Restarted Strategy: Gen 51\n",
      "--> New Popsize: 200\n",
      "Generation:  60 Perf (Best):  200.0 Perf (Mean):  197.47156 3.7782266\n",
      "Generation:  80 Perf (Best):  200.0 Perf (Mean):  199.58093 1.214557\n",
      "Generation:  100 Perf (Best):  200.0 Perf (Mean):  200.0 0.0\n",
      "--> Restarted Strategy: Gen 101\n",
      "--> New Popsize: 400\n",
      "Generation:  120 Perf (Best):  200.0 Perf (Mean):  200.0 0.0\n",
      "Generation:  140 Perf (Best):  200.0 Perf (Mean):  199.98969 0.20599204\n",
      "--> Restarted Strategy: Gen 151\n",
      "--> New Popsize: 800\n",
      "Generation:  160 Perf (Best):  200.0 Perf (Mean):  200.0 0.0\n",
      "Generation:  180 Perf (Best):  200.0 Perf (Mean):  200.0 0.0\n"
     ]
    }
   ],
   "source": [
    "num_generations = 200\n",
    "print_every_k_gens = 20\n",
    "\n",
    "es_logging = ESLog(param_reshaper.total_params,\n",
    "                   num_generations=num_generations,\n",
    "                   top_k=5,\n",
    "                   maximize=True)\n",
    "log = es_logging.initialize()\n",
    "\n",
    "fit_shaper = FitnessShaper(centered_rank=False,\n",
    "                           z_score=False,\n",
    "                           w_decay=0.1,\n",
    "                           maximize=True)\n",
    "\n",
    "state = strategy.initialize(rng, es_params)\n",
    "\n",
    "restart_gens = []\n",
    "\n",
    "for gen in range(num_generations):\n",
    "    rng, rng_init, rng_ask, rng_eval = jax.random.split(rng, 4)\n",
    "    if state.restart_state.restart_next:\n",
    "        restart_gens.append(gen)\n",
    "        print(f\"--> Restarted Strategy: Gen {gen}\")\n",
    "        print_update = True\n",
    "    else:\n",
    "        print_update = False\n",
    "    x, state = strategy.ask(rng_ask, state, es_params)\n",
    "    if print_update:\n",
    "        print(f\"--> New Popsize: {state.restart_state.active_popsize}\")\n",
    "    reshaped_params = param_reshaper.reshape(x)\n",
    "    fitness = evaluator.rollout(rng_eval, reshaped_params).mean(axis=1)\n",
    "    fit_re = fit_shaper.apply(x, fitness)\n",
    "    state = strategy.tell(x, fit_re, state, es_params)\n",
    "    log = es_logging.update(log, x, fitness)\n",
    "    \n",
    "    if gen % print_every_k_gens == 0:\n",
    "        print(\"Generation: \", gen, \"Perf (Best): \", log[\"log_top_1\"][gen], \"Perf (Mean): \", fitness.mean(), fitness.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = es_logging.plot(log, \"CartPole MLP SimpleES w. Restarts\")\n",
    "for i in restart_gens:\n",
    "    ax.axvline(i, c=\"black\", ls=\"--\", alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BIPOP-CMA-ES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapperParams(strategy_params=EvoParams(mu_eff=DeviceArray(26.966648, dtype=float32), c_1=DeviceArray(1.2144133e-06, dtype=float32), c_mu=DeviceArray(3.0331763e-05, dtype=float32), c_sigma=DeviceArray(0.02204519, dtype=float32), d_sigma=DeviceArray(1.0220451, dtype=float32), c_c=DeviceArray(0.00312667, dtype=float32), chi_n=DeviceArray(35.798046, dtype=float32, weak_type=True), c_m=1.0, sigma_init=1.0, init_min=0.0, init_max=0.0, clip_min=-3.4028235e+38, clip_max=3.4028235e+38), restart_params=RestartParams(min_num_gens=50, min_fitness_spread=1, popsize_multiplier=2, tol_x=1e-12, tol_x_up=10000.0, tol_condition_C=100000000000000.0, copy_mean=True))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from evosax import BIPOP_CMA_ES\n",
    "\n",
    "strategy = BIPOP_CMA_ES(popsize=100,\n",
    "                        num_dims=param_reshaper.total_params,\n",
    "                        elite_ratio=0.5)\n",
    "es_params = strategy.default_params.replace(\n",
    "        restart_params=strategy.default_params.restart_params.replace(\n",
    "            min_fitness_spread=1)\n",
    "        )\n",
    "es_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ParameterReshaper: 1282 parameters detected for optimization.\n"
     ]
    }
   ],
   "source": [
    "# Use single device due to odd/even popsizes -> makes it hard to make sure even division\n",
    "param_reshaper = ParameterReshaper(params, n_devices=1)\n",
    "evaluator = GymnaxFitness(\"CartPole-v1\", num_env_steps=200, num_rollouts=16, n_devices=1)\n",
    "evaluator.set_apply_fn(network.apply)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/anaconda3/envs/mle-toolbox/lib/python3.9/site-packages/flax/core/scope.py:740: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.\n",
      "  abs_value_flat = jax.tree_leaves(abs_value)\n",
      "/Users/rob/anaconda3/envs/mle-toolbox/lib/python3.9/site-packages/flax/core/scope.py:741: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.\n",
      "  value_flat = jax.tree_leaves(value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  0 Perf (Best):  149.4375 Perf (Mean):  16.563124\n",
      "Generation:  20 Perf (Best):  200.0 Perf (Mean):  49.830624\n",
      "Generation:  40 Perf (Best):  200.0 Perf (Mean):  178.595\n",
      "Generation:  60 Perf (Best):  200.0 Perf (Mean):  195.30812\n",
      "Generation:  80 Perf (Best):  200.0 Perf (Mean):  194.295\n",
      "Generation:  100 Perf (Best):  200.0 Perf (Mean):  196.60187\n",
      "Generation:  120 Perf (Best):  200.0 Perf (Mean):  186.63875\n",
      "Generation:  140 Perf (Best):  200.0 Perf (Mean):  187.41937\n",
      "Generation:  160 Perf (Best):  200.0 Perf (Mean):  196.69\n",
      "Generation:  180 Perf (Best):  200.0 Perf (Mean):  182.97624\n",
      "Generation:  200 Perf (Best):  200.0 Perf (Mean):  175.15187\n",
      "Generation:  220 Perf (Best):  200.0 Perf (Mean):  178.8075\n",
      "Generation:  240 Perf (Best):  200.0 Perf (Mean):  182.48563\n",
      "Generation:  260 Perf (Best):  200.0 Perf (Mean):  190.94063\n",
      "Generation:  280 Perf (Best):  200.0 Perf (Mean):  181.2175\n",
      "Generation:  300 Perf (Best):  200.0 Perf (Mean):  155.97063\n",
      "Generation:  320 Perf (Best):  200.0 Perf (Mean):  150.36\n",
      "Generation:  340 Perf (Best):  200.0 Perf (Mean):  116.10562\n",
      "Generation:  360 Perf (Best):  200.0 Perf (Mean):  64.515\n",
      "Generation:  380 Perf (Best):  200.0 Perf (Mean):  25.566874\n",
      "Generation:  400 Perf (Best):  200.0 Perf (Mean):  23.734999\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/y4/1lwbxdz55wzg_83326j5cjk40000gn/T/ipykernel_41931/3292922320.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0mfitness\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrollout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrng_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreshaped_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0mfit_re\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_shaper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitness\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m     \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstrategy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_re\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mes_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m     \u001b[0mlog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mes_logging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitness\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/mle-toolbox/lib/python3.9/site-packages/flax/struct.py\u001b[0m in \u001b[0;36mclz_from_iterable\u001b[0;34m(meta, data)\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m   \u001b[0;32mdef\u001b[0m \u001b[0mclz_from_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m     \u001b[0mmeta_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeta_fields\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m     \u001b[0mdata_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_fields\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "num_generations = 600\n",
    "print_every_k_gens = 20\n",
    "\n",
    "es_logging = ESLog(param_reshaper.total_params,\n",
    "                   num_generations=num_generations,\n",
    "                   top_k=5,\n",
    "                   maximize=True)\n",
    "log = es_logging.initialize()\n",
    "\n",
    "fit_shaper = FitnessShaper(w_decay=0.1,\n",
    "                           maximize=True)\n",
    "\n",
    "state = strategy.initialize(rng, es_params)\n",
    "\n",
    "restart_gens = []\n",
    "\n",
    "for gen in range(num_generations):\n",
    "    rng, rng_init, rng_ask, rng_eval = jax.random.split(rng, 4)\n",
    "    if state.restart_state.restart_next:\n",
    "        restart_gens.append(gen)\n",
    "        print(f\"--> Restarted Strategy: Gen {gen}\")\n",
    "        print_update = True\n",
    "    else:\n",
    "        print_update = False\n",
    "    x, state = strategy.ask(rng_ask, state, es_params)\n",
    "    if print_update:\n",
    "        print(f\"--> New Popsize: {state.restart_state.active_popsize}\")\n",
    "    reshaped_params = param_reshaper.reshape(x)\n",
    "    fitness = evaluator.rollout(rng_eval, reshaped_params).mean(axis=1)\n",
    "    fit_re = fit_shaper.apply(x, fitness)\n",
    "    state = strategy.tell(x, fit_re, state, es_params)\n",
    "    log = es_logging.update(log, x, fitness)\n",
    "    \n",
    "    if gen % print_every_k_gens == 0:\n",
    "        print(\"Generation: \", gen, \"Perf (Best): \", log[\"log_top_1\"][gen], \"Perf (Mean): \", fitness.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 222,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = es_logging.plot(log, \"CartPole MLP BIPOP-CMA-ES\")\n",
    "for i in restart_gens:\n",
    "    ax.axvline(i, c=\"black\", ls=\"--\", alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mle-toolbox",
   "language": "python",
   "name": "mle-toolbox"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}