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"cells": [
{
"cell_type": "markdown",
"id": "0",
"metadata": {},
"source": [
"# Solving reinforcement learning tasks using functional evolutionary algorithms\n",
"\n",
"The functional implementations of evolutionary algorithms can interact with the object-oriented `Problem` API of EvoTorch. To demonstrate this, we instantiate a `GymNE` problem configured to work on the reinforcement learning task `CartPole-v1`, and we use the functional `pgpe` algorithm to solve it."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1",
"metadata": {},
"outputs": [],
"source": [
"from evotorch.algorithms.functional import pgpe, pgpe_ask, pgpe_tell\n",
"from evotorch.neuroevolution import GymNE\n",
"from datetime import datetime\n",
"import torch"
]
},
{
"cell_type": "markdown",
"id": "2",
"metadata": {},
"source": [
"Below, we instantiate the reinforcement learning problem."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3",
"metadata": {},
"outputs": [],
"source": [
"problem = GymNE(\n",
" # The id of the gymnasium task:\n",
" \"CartPole-v1\",\n",
"\n",
" # Policy architecture to use.\n",
" # This can also be given as a subclass of `torch.nn.Module`, or an instantiated\n",
" # `torch.nn.Module` object. For simplicity, we use a basic feed-forward neural\n",
" # network, that can be expressed as a string.\n",
" \"Linear(obs_length, 16) >> Tanh() >> Linear(16, act_length)\",\n",
"\n",
" # Setting `observation_normalization` as True means that stats regarding\n",
" # observations will be collected during each population evaluation\n",
" # process, and those stats will be used to normalize the future\n",
" # observation data (that will be given as input to the policy).\n",
" observation_normalization=True,\n",
"\n",
" # Number of actors to be used. Can be an integer.\n",
" # The string \"max\" means that the number of actors will be equal to the\n",
" # number of CPUs.\n",
" num_actors=\"max\",\n",
")\n",
"\n",
"problem"
]
},
{
"cell_type": "markdown",
"id": "4",
"metadata": {},
"source": [
"Now that we have instantiated our problem, we make a callable evaluator object from it. This callable evaluator, named `f`, behaves like a function `f(x)`, where `x` can be a single solution (represented by a 1-dimensional tensor), or a population (represented by a 2-dimensional tensor where each row is a solution), or a batch of populations (represented by a tensor with at least 3 dimensions). Upon receiving its argument `x`, `f` uses the problem object to evaluate the solution(s), and return the evalution result(s)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5",
"metadata": {},
"outputs": [],
"source": [
"f = problem.make_callable_evaluator()\n",
"f"
]
},
{
"cell_type": "markdown",
"id": "6",
"metadata": {},
"source": [
"Hyperparameters for `pgpe`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7",
"metadata": {},
"outputs": [],
"source": [
"popsize = 100\n",
"center_init = problem.make_zeros(num_solutions=1)[0]\n",
"max_speed = 0.15\n",
"center_learning_rate = max_speed * 0.75\n",
"radius = max_speed * 15\n",
"stdev_learning_rate = 0.1\n",
"stdev_max_change = 0.2"
]
},
{
"cell_type": "markdown",
"id": "8",
"metadata": {},
"source": [
"We prepare the `pgpe` algorithm and get its initial state:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9",
"metadata": {},
"outputs": [],
"source": [
"pgpe_state = pgpe(\n",
" # Center of the initial search distribution\n",
" center_init=center_init,\n",
"\n",
" # Radius for the initial search distribution\n",
" radius_init=radius,\n",
"\n",
" # Learning rates for when updating the center and the standard deviation\n",
" # of the search distribution\n",
" center_learning_rate=center_learning_rate,\n",
" stdev_learning_rate=stdev_learning_rate,\n",
"\n",
" # Maximum relative amount of change for standard deviation.\n",
" # Setting this as 0.2 means that an item of the standard deviation vector\n",
" # will not be allowed to change more than the 20% of its original value.\n",
" stdev_max_change=stdev_max_change,\n",
"\n",
" # The ranking method to be used.\n",
" # \"centered\" is a ranking method which assigns the rank -0.5 to the worst\n",
" # solution, and +0.5 to the best solution.\n",
" ranking_method=\"centered\",\n",
"\n",
" # The optimizer to be used. Can be \"clipup\", \"adam\", or \"sgd\".\n",
" optimizer=\"clipup\",\n",
"\n",
" # Optimizer-specific hyperparameters:\n",
" optimizer_config={\"max_speed\": max_speed},\n",
"\n",
" # Whether or not symmetric sampling will be used.\n",
" symmetric=True,\n",
"\n",
" # We want to maximize the evaluation results.\n",
" # In the case of reinforcement learning tasks declared via `GymNE`,\n",
" # evaluation results represent the cumulative rewards.\n",
" objective_sense=\"max\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "10",
"metadata": {},
"source": [
"Below is the main loop of the evolutionary search."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "11",
"metadata": {},
"outputs": [],
"source": [
"# We run the evolutionary search for this many generations:\n",
"num_generations = 40\n",
"\n",
"last_report_time = datetime.now()\n",
"\n",
"# This is the interval (in seconds) for reporting the status:\n",
"reporting_interval = 1\n",
"\n",
"for generation in range(1, 1 + num_generations):\n",
" # Get a population from the pgpe algorithm\n",
" population = pgpe_ask(pgpe_state, popsize=popsize)\n",
"\n",
" # Evaluate the fitnesses\n",
" fitnesses = f(population)\n",
"\n",
" # Inform pgpe of the fitnesses and get its next state\n",
" pgpe_state = pgpe_tell(pgpe_state, population, fitnesses)\n",
"\n",
" # If it is time to report, print the status\n",
" tnow = datetime.now()\n",
" if (tnow - last_report_time).total_seconds() > reporting_interval:\n",
" print(\"generation:\", generation, \"median eval:\", torch.median(fitnesses))\n",
" last_report_time = tnow"
]
},
{
"cell_type": "markdown",
"id": "12",
"metadata": {},
"source": [
"Here is the center point of the most recent search distribution:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "13",
"metadata": {},
"outputs": [],
"source": [
"x = pgpe_state.optimizer_state.center\n",
"x"
]
},
{
"cell_type": "markdown",
"id": "14",
"metadata": {},
"source": [
"Now, we visualize the agent evolved by our functional `pgpe`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15",
"metadata": {},
"outputs": [],
"source": [
"problem.visualize(x)"
]
}
],
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