{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"execution": {},
"id": "view-in-github"
},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"# NMA Robolympics: Controlling robots using reinforcement learning\n",
"\n",
"**By Neuromatch Academy**\n",
"\n",
"__Content creators:__ Roman Vaxenburg, Diptodip Deb, Srinivas Turaga\n",
"\n",
"__Production editors:__ Spiros Chavlis"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# Objective\n",
"\n",
"This notebook provides a minimal but complete example of the reinforcement learning infrastructure, training sequence, and result visualization. We will use a [pybullet](https://github.com/bulletphysics/bullet3) locomotion environment and [dm-acme](https://github.com/deepmind/acme) reinforcement learning agents to learn a policy to perform a simple task with the 2D `Hopper` robot.\n",
"\n",
"We will show how to create and inspect the environment and how to start modifying it to have robots perform various tasks. This example should provide a good starting point for your own exploration!\n",
"\n",
"Even though this example uses a very simple robot, you can start experimenting with more complicated ones, such as `Ant` and `Humanoid` by just importing and modifying them as shown in this example. Also, start exploring the source code of the environments so you can modify them more easily later.\n",
"\n",
"We would also suggest going over the `dm-acme` [tutorial notebook](https://github.com/deepmind/acme/blob/master/examples/tutorial.ipynb).\n",
"\n",
"For a general introduction to Reinforcement Learning, it's worth checking out [this course](https://deepmind.com/learning-resources/-introduction-reinforcement-learning-david-silver).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"# Colab limits\n",
"\n",
"Please note that due to the Colab usage limits on the one hand, and the compute requirements of the project on the other hand, most likely you won't be able to leverage Colab's GPU for a sufficient amount of time. Instead, we suggest working in CPU-only mode (it shouldn't slow you down very much, typical RL workloads are CPU-bound anyway). Make sure you're not using GPU by doing Runtime -> Change runtime type -> Hardware accelerator -> None.\n",
"\n",
"Also, when instantiating the environments, make sure to keep the default setting `render=False`."
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Install dependencies\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" In the first cell we'll install all of the necessary dependencies.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"execution": {},
"tags": [
"hide-input"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\u001b[?25h Building wheel for dm-acme (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.25.1 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.4/268.4 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for pybullet (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
]
}
],
"source": [
"# @title Install dependencies\n",
"# @markdown In the first cell we'll install all of the necessary dependencies.\n",
"!apt-get update > /dev/null 2>&1\n",
"!apt-get -y install ffmpeg freeglut3-dev xvfb > /dev/null 2>&1 # For visualization.\n",
"!pip install imageio-ffmpeg --quiet\n",
"\n",
"!pip install jedi --quiet\n",
"!pip install --upgrade pip setuptools wheel --quiet\n",
"!pip install dm-acme[jax] --quiet\n",
"!pip install dm-sonnet --quiet\n",
"!pip install trfl --quiet\n",
"!pip install pybullet --quiet"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/gym/envs/registration.py:440: UserWarning: \u001b[33mWARN: The `registry.env_specs` property along with `EnvSpecTree` is deprecated. Please use `registry` directly as a dictionary instead.\u001b[0m\n",
" logger.warn(\n",
"/usr/local/lib/python3.10/dist-packages/tensorflow_probability/python/__init__.py:57: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
" if (distutils.version.LooseVersion(tf.__version__) <\n",
"/usr/local/lib/python3.10/dist-packages/reverb/platform/default/ensure_tf_install.py:53: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
" if (distutils.version.LooseVersion(version) <\n"
]
}
],
"source": [
"# Imports\n",
"import os\n",
"import shutil\n",
"import matplotlib\n",
"import pybullet_envs\n",
"\n",
"from acme.utils import loggers\n",
"from acme.tf import networks\n",
"from acme.tf import utils as tf2_utils\n",
"from acme.agents.tf.d4pg import D4PG\n",
"from acme.agents.tf.ddpg import DDPG\n",
"from acme.agents.tf.dmpo import DistributionalMPO\n",
"from acme import wrappers, specs, environment_loop\n",
"\n",
"import numpy as np\n",
"import sonnet as snt\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.animation as animation\n",
"\n",
"from google.colab import drive\n",
"from IPython.display import HTML"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import `pybullet` locomotion environments\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"execution": {},
"tags": [
"hide-input"
]
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
" and should_run_async(code)\n"
]
}
],
"source": [
"# @title Import `pybullet` locomotion environments\n",
"\n",
"from pybullet_envs.gym_locomotion_envs import HopperBulletEnv\n",
"from pybullet_envs.gym_locomotion_envs import Walker2DBulletEnv\n",
"from pybullet_envs.gym_locomotion_envs import HalfCheetahBulletEnv\n",
"from pybullet_envs.gym_locomotion_envs import AntBulletEnv\n",
"from pybullet_envs.gym_locomotion_envs import HumanoidBulletEnv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Figure settings\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"execution": {},
"tags": [
"hide-input"
]
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/matplotlib_inline/config.py:68: DeprecationWarning: InlineBackend._figure_format_changed is deprecated in traitlets 4.1: use @observe and @unobserve instead.\n",
" def _figure_format_changed(self, name, old, new):\n"
]
}
],
"source": [
"# @title Figure settings\n",
"import ipywidgets as widgets # interactive display\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"plt.style.use(\"https://raw.githubusercontent.com/NeuromatchAcademy/content-creation/main/nma.mplstyle\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# Functions for saving and restoring checkpoints\n",
"Due to Colab usage limits, the Colab runtime will be restarting periodically. In order to preserve the most recent training checkpoint during a restart, please use the functions below.\n",
"\n",
"To do so, you'll have to first mount Google Drive (will be shown below).\n",
"\n",
"### Before runtime restart:\n",
"Use `save_ckpt_to_drive` to locate the checkpoint and save it to your Google Drive in a directory `/acme_ckpt`\n",
"\n",
"### After runtime restart:\n",
"Use `restore_ckpt_from_drive` to recover the checkpoint from Google Drive and copy it back to the restarted Colab virtual machine."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
" and should_run_async(code)\n"
]
}
],
"source": [
"def save_ckpt_to_drive(agent):\n",
" \"\"\"Saves agent checkpoint directory to Google Drive.\n",
"\n",
" WARNING: Will replace the entire content of the\n",
" drive directory `/root/drive/MyDrive/acme_ckpt`.\n",
"\n",
" Args:\n",
" agent: core.Actor\n",
" \"\"\"\n",
" src = agent._learner._checkpointer._checkpoint_manager.directory\n",
" dst = '/root/drive/MyDrive/acme_ckpt'\n",
" if os.path.exists(dst):\n",
" shutil.rmtree(dst)\n",
" shutil.copytree(src, dst)\n",
" print(f'Saved {src} to {dst}')\n",
"\n",
"\n",
"def restore_ckpt_from_drive(agent):\n",
" \"\"\"Restores agent checkpoint directory from Google Drive.\n",
"\n",
" The name of the local checkpoint directory will be different\n",
" than it was when the checkpoint was originally saved.\n",
" This is because `acme` checkpoiner creates a new directory\n",
" upon restart.\n",
"\n",
" WARNING: Will replace the entire content of the local\n",
" checkpoint directory (if it exists already).\n",
"\n",
" Args:\n",
" agent: core.Actor\n",
" \"\"\"\n",
" src = '/root/drive/MyDrive/acme_ckpt'\n",
" dst = agent._learner._checkpointer._checkpoint_manager.directory\n",
" if os.path.exists(dst):\n",
" shutil.rmtree(dst)\n",
" shutil.copytree(src, dst)\n",
" print(f'Restored {dst} from {src}')"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# Convenience function for creating videos\n",
"\n",
"Use this function to generate videos of your experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [],
"source": [
"def display_video(frames, framerate=30):\n",
" \"\"\"Generates video from `frames`.\n",
"\n",
" Args:\n",
" frames (ndarray): Array of shape (n_frames, height, width, 3).\n",
" framerate (int): Frame rate in units of Hz.\n",
"\n",
" Returns:\n",
" Display object.\n",
" \"\"\"\n",
" height, width, _ = frames[0].shape\n",
" dpi = 70\n",
" orig_backend = matplotlib.get_backend()\n",
" matplotlib.use('Agg') # Switch to headless 'Agg' to inhibit figure rendering.\n",
" fig, ax = plt.subplots(1, 1, figsize=(width / dpi, height / dpi), dpi=dpi)\n",
" matplotlib.use(orig_backend) # Switch back to the original backend.\n",
" ax.set_axis_off()\n",
" ax.set_aspect('equal')\n",
" ax.set_position([0, 0, 1, 1])\n",
" im = ax.imshow(frames[0])\n",
" def update(frame):\n",
" im.set_data(frame)\n",
" return [im]\n",
" interval = 1000/framerate\n",
" anim = animation.FuncAnimation(fig=fig, func=update, frames=frames,\n",
" interval=interval, blit=True, repeat=False)\n",
" return HTML(anim.to_html5_video())"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# Network factory methods for select continuous control agents\n",
"\n",
"The functions below initialize and return the policy and critic networks for several continuous control agents (DDPG, D4PG, DMPO) used later in this notebook. Feel free to explore other agents as well. For more information on these and other agents, their implementations, and links to their corresponding papers see [here](https://github.com/deepmind/acme/tree/master/acme/agents).\n",
"\n",
"Please note that the hyperparameters `layer_sizes`, `vmin`, `vmax`, `num_atoms` are set to reasonable default values, but may reqiure adjustment. Especially, `vmin` and `vmax` should be used with care. Please see the [acme github repo](https://github.com/deepmind/acme/blob/master/acme/agents/tf/d4pg/README.md) for more information."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [],
"source": [
"def make_networks_d4pg(action_spec,\n",
" policy_layer_sizes=(256, 256, 256),\n",
" critic_layer_sizes=(512, 512, 256),\n",
" vmin=-150.,\n",
" vmax=150.,\n",
" num_atoms=51,\n",
" ):\n",
" \"\"\"Networks for D4PG agent.\"\"\"\n",
" action_size = np.prod(action_spec.shape, dtype=int)\n",
"\n",
" policy_network = snt.Sequential([\n",
" tf2_utils.batch_concat,\n",
" networks.LayerNormMLP(layer_sizes=policy_layer_sizes + (action_size,)),\n",
" networks.TanhToSpec(spec=action_spec)\n",
" ])\n",
" critic_network = snt.Sequential([\n",
" networks.CriticMultiplexer(\n",
" action_network=networks.ClipToSpec(action_spec),\n",
" critic_network=networks.LayerNormMLP(\n",
" layer_sizes=critic_layer_sizes,\n",
" activate_final=True),\n",
" ),\n",
" networks.DiscreteValuedHead(vmin=vmin,\n",
" vmax=vmax,\n",
" num_atoms=num_atoms)\n",
" ])\n",
"\n",
" return policy_network, critic_network\n",
"\n",
"\n",
"def make_networks_ddpg(action_spec,\n",
" policy_layer_sizes=(256, 256, 256),\n",
" critic_layer_sizes=(512, 512, 256),\n",
" ):\n",
" \"\"\"Networks for DDPG agent.\"\"\"\n",
" action_size = np.prod(action_spec.shape, dtype=int)\n",
"\n",
" policy_network = snt.Sequential([\n",
" tf2_utils.batch_concat,\n",
" networks.LayerNormMLP(layer_sizes=policy_layer_sizes + (action_size,)),\n",
" networks.TanhToSpec(spec=action_spec)\n",
" ])\n",
" critic_network = networks.CriticMultiplexer(\n",
" action_network=networks.ClipToSpec(action_spec),\n",
" critic_network=networks.LayerNormMLP(\n",
" layer_sizes=critic_layer_sizes + (1,),\n",
" activate_final=False),\n",
" )\n",
"\n",
" return policy_network, critic_network\n",
"\n",
"\n",
"def make_networks_dmpo(action_spec,\n",
" policy_layer_sizes=(256, 256, 256),\n",
" critic_layer_sizes=(512, 512, 256),\n",
" vmin=-150.,\n",
" vmax=150.,\n",
" num_atoms=51,\n",
" ):\n",
" \"\"\"Networks for DMPO agent.\"\"\"\n",
" action_size = np.prod(action_spec.shape, dtype=int)\n",
"\n",
" policy_network = snt.Sequential([\n",
" tf2_utils.batch_concat,\n",
" networks.LayerNormMLP(layer_sizes=policy_layer_sizes,\n",
" activate_final=True),\n",
" networks.MultivariateNormalDiagHead(\n",
" action_size,\n",
" min_scale=1e-6,\n",
" tanh_mean=False,\n",
" init_scale=0.7,\n",
" fixed_scale=False,\n",
" use_tfd_independent=True)\n",
" ])\n",
"\n",
" # The multiplexer concatenates the (maybe transformed) observations/actions.\n",
" critic_network = networks.CriticMultiplexer(\n",
" action_network=networks.ClipToSpec(action_spec),\n",
" critic_network=networks.LayerNormMLP(layer_sizes=critic_layer_sizes,\n",
" activate_final=True),\n",
" )\n",
" critic_network = snt.Sequential([\n",
" critic_network,\n",
" networks.DiscreteValuedHead(vmin=vmin,\n",
" vmax=vmax,\n",
" num_atoms=num_atoms)\n",
" ])\n",
"\n",
" return policy_network, critic_network"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# List of all `pybullet` environments\n",
"\n",
"You can print the full list of environments by running the cell below. Only a subset of them are locomotion environments but feel free to explore the other ones if you're interested."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/gym/envs/registration.py:421: UserWarning: \u001b[33mWARN: The `registry.all` method is deprecated. Please use `registry.values` instead.\u001b[0m\n",
" logger.warn(\n"
]
},
{
"data": {
"text/plain": [
"['- HumanoidDeepMimicBackflipBulletEnv-v1',\n",
" '- HumanoidDeepMimicWalkBulletEnv-v1',\n",
" '- CartPoleBulletEnv-v1',\n",
" '- CartPoleContinuousBulletEnv-v0',\n",
" '- MinitaurBulletEnv-v0',\n",
" '- MinitaurBulletDuckEnv-v0',\n",
" '- RacecarBulletEnv-v0',\n",
" '- RacecarZedBulletEnv-v0',\n",
" '- KukaBulletEnv-v0',\n",
" '- KukaCamBulletEnv-v0',\n",
" '- InvertedPendulumBulletEnv-v0',\n",
" '- InvertedDoublePendulumBulletEnv-v0',\n",
" '- InvertedPendulumSwingupBulletEnv-v0',\n",
" '- ReacherBulletEnv-v0',\n",
" '- PusherBulletEnv-v0',\n",
" '- ThrowerBulletEnv-v0',\n",
" '- Walker2DBulletEnv-v0',\n",
" '- HalfCheetahBulletEnv-v0',\n",
" '- AntBulletEnv-v0',\n",
" '- HopperBulletEnv-v0',\n",
" '- HumanoidBulletEnv-v0',\n",
" '- HumanoidFlagrunBulletEnv-v0',\n",
" '- HumanoidFlagrunHarderBulletEnv-v0',\n",
" '- MinitaurExtendedEnv-v0',\n",
" '- MinitaurReactiveEnv-v0',\n",
" '- MinitaurBallGymEnv-v0',\n",
" '- MinitaurTrottingEnv-v0',\n",
" '- MinitaurStandGymEnv-v0',\n",
" '- MinitaurAlternatingLegsEnv-v0',\n",
" '- MinitaurFourLegStandEnv-v0',\n",
" '- KukaDiverseObjectGrasping-v0']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pybullet_envs.getList()"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# Modifying the environment base class\n",
"\n",
"You may start your exploration of the `pybullet` locomotion environment code from this entry point, going up and down the hierarchy of classes: see [here](https://github.com/bulletphysics/bullet3/blob/master/examples/pybullet/gym/pybullet_envs/gym_locomotion_envs.py).\n",
"\n",
"For our experiments, we will be using the `pybullet` locomotion environments with several different robots (`Hopper`, `Ant`, `Humanoid`, etc.). In order to have the robots perform different tasks, we'll need to modify some parts of the environments' code. This will (mainly) amount to modifying the environments' reward calculation in the `step` method.\n",
"\n",
"In the cell below we provide a minimal example modifying the `HopperBulletEnv` environment class. Normally, to create a Hopper environment you would just create an instance of the HopperBulletEnv class:\n",
"\n",
"```\n",
"env = HopperBulletEnv()\n",
"```\n",
"\n",
"However, if you analyze the environment's code, you'll realize that making changes (such as modifying the reward calculation) is difficult in this way. Instead, it's useful to create a custom child class inheriting from the original `HopperBulletEnv` class and override some of its methods. This subclassing will allow you to easily access the interesting pieces of the environment class to modify.\n",
"\n",
"In the example of a custom `Hopper` class below, we override several methods of its parent class to (i) make the reward calculation modifiable, (ii) add `step_counter` to enforce fixed episode duration, and (iii) make the episode termination conditions modifiable. Please note that in some cases the overriding methods still call their parent methods after executing the required modifications (such as the `__init__`, `reset`, `_isDone` methods do.). In contrast, the `step` method is overriden in its entirety and doesn't reference its parent method. So instead of the code line above, the environment would be created as:\n",
"\n",
"```\n",
"env = Hopper()\n",
"```\n",
"\n",
"You can use this approach and this example as the starting point of your project. In many cases, this custom class can be used as is (with only a name change) with other robots in the `pybullet` locomotion environments by inheriting from their respective original environment classes instead of from `HopperBulletEnv`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [],
"source": [
"class Hopper(HopperBulletEnv):\n",
"\n",
" def __init__(self, render=False, episode_steps=1000):\n",
" \"\"\"Modifies `__init__` in `HopperBulletEnv` parent class.\"\"\"\n",
" self.episode_steps = episode_steps\n",
" super().__init__(render=render)\n",
"\n",
" def reset(self):\n",
" \"\"\"Modifies `reset` in `WalkerBaseBulletEnv` base class.\"\"\"\n",
" self.step_counter = 0\n",
" return super().reset()\n",
"\n",
" def _isDone(self):\n",
" \"\"\"Modifies `_isDone` in `WalkerBaseBulletEnv` base class.\"\"\"\n",
" return (self.step_counter == self.episode_steps\n",
" or super()._isDone())\n",
"\n",
" def step(self, a):\n",
" \"\"\"Fully overrides `step` in `WalkerBaseBulletEnv` base class.\"\"\"\n",
"\n",
" self.step_counter += 1\n",
"\n",
" # if multiplayer, action first applied to all robots,\n",
" # then global step() called, then _step() for all robots\n",
" # with the same actions\n",
" if not self.scene.multiplayer:\n",
" self.robot.apply_action(a)\n",
" self.scene.global_step()\n",
"\n",
" state = self.robot.calc_state() # also calculates self.joints_at_limit\n",
"\n",
" # state[0] is body height above ground, body_rpy[1] is pitch\n",
" self._alive = float(self.robot.alive_bonus(state[0] + self.robot.initial_z,\n",
" self.robot.body_rpy[1]))\n",
" done = self._isDone()\n",
" if not np.isfinite(state).all():\n",
" print(\"~INF~\", state)\n",
" done = True\n",
"\n",
" potential_old = self.potential\n",
" self.potential = self.robot.calc_potential()\n",
" progress = float(self.potential - potential_old)\n",
"\n",
" feet_collision_cost = 0.0\n",
" for i, f in enumerate(self.robot.feet):\n",
" contact_ids = set((x[2], x[4]) for x in f.contact_list())\n",
" if (self.ground_ids & contact_ids):\n",
" self.robot.feet_contact[i] = 1.0\n",
" else:\n",
" self.robot.feet_contact[i] = 0.0\n",
"\n",
" # let's assume we have DC motor with controller, and reverse current braking\n",
" electricity_cost = self.electricity_cost * float(\n",
" np.abs(a * self.robot.joint_speeds).mean())\n",
" electricity_cost += self.stall_torque_cost * float(np.square(a).mean())\n",
"\n",
" joints_at_limit_cost = float(self.joints_at_limit_cost * self.robot.joints_at_limit)\n",
"\n",
" self.rewards = [\n",
" self._alive, progress, electricity_cost,\n",
" joints_at_limit_cost, feet_collision_cost\n",
" ]\n",
" self.HUD(state, a, done)\n",
" self.reward += sum(self.rewards)\n",
"\n",
" return state, sum(self.rewards), bool(done), {}"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# Instantiate the environment\n",
"\n",
"Here, we are creating an example `Hopper` environment. Once created, we are wrapping it with `GymWrapper` to make the native `Gym` environment interface compatible with the one used in the `dm-acme` library, which is the reinforcement learning package that we will be using. `dm-acme` adheres to the interface defined [here](https://github.com/deepmind/dm_env). Finally, we also use `SinglePrecisionWrapper` to enforce single-precision on a potentially double-precision environment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [],
"source": [
"env = Hopper(render=False)\n",
"\n",
"env = wrappers.GymWrapper(env)\n",
"env = wrappers.SinglePrecisionWrapper(env)\n",
"\n",
"action_spec = env.action_spec() # Specifies action shape and dimensions.\n",
"env_spec = specs.make_environment_spec(env) # Environment specifications."
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# The default task"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"If not modified, the default task of the `HopperBulletEnv` environment is to have the robot hop to the target location located 1 km away. The target location is stored as an attribute of the `robot` object and can be accessed as in the cell below.\n",
"\n",
"The task also contains other constraints, such as `electricity_cost`, and certain episode termination conditions. Please start from our custom `Hopper` class in the above cell and work your way backwards to the the environment code for more details!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"data": {
"text/plain": [
"(1000.0, 0)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# x and y coordinates of the target location.\n",
"env.robot.walk_target_x, env.robot.walk_target_y"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# Let's inspect the environment a bit\n",
"\n",
"#### Plot one frame of the environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/gym/core.py:43: DeprecationWarning: \u001b[33mWARN: The argument mode in render method is deprecated; use render_mode during environment initialization instead.\n",
"See here for more information: https://www.gymlibrary.ml/content/api/\u001b[0m\n",
" deprecation(\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 575,
"width": 760
}
},
"output_type": "display_data"
}
],
"source": [
"_ = env.reset()\n",
"\n",
"frame = env.environment.render(mode='rgb_array')\n",
"plt.imshow(frame)\n",
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"## Run the environment with random actions\n",
"\n",
"We haven't trained the policy yet, but we can still see the environment in action by passing a random control sequence to it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" Your browser does not support the video tag.\n",
" "
],
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run env for n_steps, apply random actions, and show video.\n",
"n_steps = 200\n",
"\n",
"frames = []\n",
"timestep = env.reset()\n",
"for _ in range(n_steps):\n",
" # Random control of actuators.\n",
" action = np.random.uniform(action_spec.minimum,\n",
" action_spec.maximum,\n",
" size=action_spec.shape)\n",
" timestep = env.step(action)\n",
" frames.append(env.environment.render(mode='rgb_array'))\n",
"\n",
"display_video(frames, framerate=20)"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"## Lets take a look at some other environment properties\n",
"\n",
"Notice the shapes and min/max limits"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Actions:\n",
" BoundedArray(shape=(3,), dtype=dtype('float32'), name='action', minimum=[-1. -1. -1.], maximum=[1. 1. 1.])\n",
"Observations:\n",
" BoundedArray(shape=(15,), dtype=dtype('float32'), name='observation', minimum=[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n",
" -inf], maximum=[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf])\n",
"Rewards:\n",
" Array(shape=(), dtype=dtype('float32'), name='reward')\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
" and should_run_async(code)\n"
]
}
],
"source": [
"print('Actions:\\n', env_spec.actions)\n",
"print('Observations:\\n', env_spec.observations)\n",
"print('Rewards:\\n', env_spec.rewards)"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"## Inspect some of the robot properties\n",
"\n",
"Let's examine the (Cartesian) coordinates of different body parts of the robot and its speed. Notice that `robot` is an attribute of the `env` class. Also, note how the body parts are accessed as you may need it for adjusting the reward calculation in your project.\n",
"\n",
"Feel free to explore other properties (attributes) of the `env.robot` object, such as velocities, joint angles, etc."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"link0_2 [0.2868544 0. 0. ]\n",
"torso [-0.0166108 0. 1.2329636]\n",
"link0_3 [0.2868544 0. 0.02035943]\n",
"link0_4 [0.2868544 0. 0.02035943]\n",
"link0_6 [0.03194364 0. 1.03894688]\n",
"thigh [0.03755892 0. 0.814017 ]\n",
"link0_8 [0.0431742 0. 0.58908712]\n",
"leg [0.05006377 0. 0.33918206]\n",
"link0_10 [0.05695333 0. 0.089277 ]\n",
"foot [0.12194239 0. 0.09046921]\n",
"floor [0. 0. 0.]\n"
]
}
],
"source": [
"# Cartesian coordinates of body parts.\n",
"for body_part in env.robot.parts.keys():\n",
" print(f\"{body_part:10} {env.robot.parts[body_part].pose().xyz()}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"data": {
"text/plain": [
"array([-0.39135196, 0. , -0.97286361])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cartesian components of robot speed.\n",
"env.robot_body.speed()"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# Create the `dm-acme` agent\n",
"\n",
"Now we are ready to create the agent. Below we provide examples of creating instances of three select agents (DDPG, D4PG, DMPO) that we implemented above. Please feel free to explore other agents as well. For more information on these and other agents, their implementations, and links to their corresponding papers see the acme github [repo](https://github.com/deepmind/acme/tree/master/acme/agents).\n",
"\n",
"The direct links to the implementation of these three agents for you to start exploring are:\n",
"\n",
"- [Deep Deterministic Policy Gradient (DDPG) agent](https://github.com/deepmind/acme/blob/master/acme/agents/tf/ddpg/agent.py)\n",
"\n",
"- [Distributed Distributional Deep Determinist (D4PG) agent](https://github.com/deepmind/acme/blob/master/acme/agents/tf/d4pg/agent.py)\n",
"\n",
"- [Distributional Maximum a posteriori Policy Optimisation (DMPO) agent](https://github.com/deepmind/acme/blob/master/acme/agents/tf/dmpo/agent.py)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"First, lets configure loggers and optimizers:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [],
"source": [
"learner_log_every = 60. # Learner logging frequency, seconds.\n",
"loop_log_every = 60. # Environment loop logging frequency, seconds.\n",
"checkpoint = True # Checkpoint saved every 10 minutes.\n",
"\n",
"learner_logger = loggers.TerminalLogger(label='Learner',\n",
" time_delta=learner_log_every,\n",
" print_fn=print)\n",
"loop_logger = loggers.TerminalLogger(label='Environment Loop',\n",
" time_delta=loop_log_every,\n",
" print_fn=print)\n",
"\n",
"# Note: optimizers can be passed only to the D4PG and DMPO agents.\n",
"# The optimizer for DDPG is hard-coded in the agent class.\n",
"policy_optimizer = snt.optimizers.Adam(1e-4)\n",
"critic_optimizer = snt.optimizers.Adam(1e-4)"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# D4PG agent"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"As an example, in the next cell we instantiate the D4PG agent. Examples of other agents (DDPG, DMPO) are provided at the end of the notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [],
"source": [
"# Create networks.\n",
"policy_network, critic_network = make_networks_d4pg(action_spec)\n",
"\n",
"# Create agent.\n",
"agent = D4PG(environment_spec=env_spec,\n",
" policy_network=policy_network,\n",
" critic_network=critic_network,\n",
" observation_network=tf2_utils.batch_concat, # Identity Op.\n",
" policy_optimizer=policy_optimizer,\n",
" critic_optimizer=critic_optimizer,\n",
" logger=learner_logger,\n",
" checkpoint=checkpoint)"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# Training\n",
"\n",
"Finally, we are ready to start training!\n",
"\n",
"Please refer to the [source code](https://github.com/deepmind/acme/blob/master/acme/environment_loop.py) to see how to use the environment loop.\n",
"\n",
"The training checkpoint (containing the network weights, optimizer parameters, etc.) will be saved every 10 minutes. Please remember to save and then restore the checkpoint from Google Drive if you are restarting the Colab runtime. See example below."
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"*Note:* `num_steps = 100_000` but we reduce it to 1000 to reduce computational time. Please change it back if you want to see the original output of the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Calling GradientTape.gradient on a persistent tape inside its context is significantly less efficient than calling it outside the context (it causes the gradient ops to be recorded on the tape, leading to increased CPU and memory usage). Only call GradientTape.gradient inside the context if you actually want to trace the gradient in order to compute higher order derivatives.\n"
]
}
],
"source": [
"num_steps = 1000 # 100_000 # Number of environment loop steps. Adjust as needed!\n",
"\n",
"loop = environment_loop.EnvironmentLoop(env, agent, logger=loop_logger)\n",
"\n",
"# Start training!\n",
"loop.run(num_episodes=None,\n",
" num_steps=num_steps)"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"## Examine trained policy\n",
"\n",
"As the policy has (hopefully) been trained by now, let's test it in the environment and examine the result.\n",
"\n",
"Note that we will also collect the reward at each timestep and plot it later."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
" and should_run_async(code)\n",
"/usr/local/lib/python3.10/dist-packages/gym/core.py:43: DeprecationWarning: \u001b[33mWARN: The argument mode in render method is deprecated; use render_mode during environment initialization instead.\n",
"See here for more information: https://www.gymlibrary.ml/content/api/\u001b[0m\n",
" deprecation(\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" Your browser does not support the video tag.\n",
" "
],
"text/plain": [
""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run the environment with the learned policy and display video.\n",
"n_steps = 500\n",
"\n",
"frames = [] # Frames for video.\n",
"reward = [[]] # Reward at every timestep.\n",
"timestep = env.reset()\n",
"for _ in range(n_steps):\n",
" frames.append(env.environment.render(mode='rgb_array').copy())\n",
" action = agent.select_action(timestep.observation)\n",
" timestep = env.step(action)\n",
"\n",
" # `timestep.reward` is None when episode terminates.\n",
" if timestep.reward:\n",
" # Old episode continues.\n",
" reward[-1].append(timestep.reward.item())\n",
" else:\n",
" # New episode begins.\n",
" reward.append([])\n",
"\n",
"display_video(frames)"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"## Plot the reward\n",
"\n",
"Each color represent a separate episode."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
" and should_run_async(code)\n"
]
},
{
"data": {
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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 575,
"width": 774
}
},
"output_type": "display_data"
}
],
"source": [
"env_step = 0\n",
"for episode in reward:\n",
" plt.plot(np.arange(env_step, env_step+len(episode)), episode)\n",
" env_step += len(episode)\n",
"plt.xlabel('Timestep', fontsize=14)\n",
"plt.ylabel('Reward', fontsize=14)\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"## Total reward\n",
"\n",
"Finally, let's print the total reward for the test episodes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total reward in episode 0: 11.70\n",
"Total reward in episode 1: 4.51\n",
"Total reward in episode 2: 23.35\n",
"Total reward in episode 3: 24.61\n",
"Total reward in episode 4: 14.96\n",
"Total reward in episode 5: 19.71\n",
"Total reward in episode 6: 14.93\n",
"Total reward in episode 7: 8.25\n",
"Total reward in episode 8: 6.65\n",
"Total reward in episode 9: 12.73\n",
"Total reward in episode 10: 5.31\n",
"Total reward in episode 11: 13.41\n",
"Total reward in episode 12: 14.97\n",
"Total reward in episode 13: 18.16\n",
"Total reward in episode 14: 10.22\n"
]
}
],
"source": [
"for i, episode in enumerate(reward):\n",
" print(f\"Total reward in episode {i}: {sum(episode):.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"## Saving and restoring training checkpoints to/from Google Drive\n",
"\n",
"To avoid losing the training checkpoints during runtime restart, follow these steps:"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"### 1. Mount drive to temporarily save checkpoints"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [],
"source": [
"# Mount drive. -- You may want to add your gDrive\n",
"# drive.mount('/root/drive')"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"### 2. BEFORE restarting the runtime, save checkpoint to drive"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved /root/acme/81b1f746-216e-11ee-93ef-0242ac1c000c/checkpoints/d4pg_learner to /root/drive/MyDrive/acme_ckpt\n"
]
}
],
"source": [
"# Save agent checkpoint to drive.\n",
"save_ckpt_to_drive(agent)"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"### 3. AFTER restarting the runtime, restore checkpoint from drive\n",
"\n",
"To restore a checkpoint in the restarted Colab runtime:\n",
"\n",
"1. Re-install all the libraries and run all the cells as before, including the agent instantiation, **except** the training cell.\n",
"2. Run the cell below.\n",
"3. Run the cell that instantiates the agent **again**."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Restored /root/acme/81b1f746-216e-11ee-93ef-0242ac1c000c/checkpoints/d4pg_learner from /root/drive/MyDrive/acme_ckpt\n"
]
}
],
"source": [
"# Restore checkpoint from drive.\n",
"restore_ckpt_from_drive(agent)"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"### 4. Optionally, unmount drive"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Drive not mounted, so nothing to flush and unmount.\n"
]
}
],
"source": [
"# Unmount drive.\n",
"drive.flush_and_unmount()"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# Examples of two additional agents:"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"## DMPO agent"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow_probability/python/distributions/distribution.py:342: calling Independent.__init__ (from tensorflow_probability.python.distributions.independent) with reinterpreted_batch_ndims=None is deprecated and will be removed after 2022-03-01.\n",
"Instructions for updating:\n",
"Please pass an integer value for `reinterpreted_batch_ndims`. The current behavior corresponds to `reinterpreted_batch_ndims=tf.size(distribution.batch_shape_tensor()) - 1`.\n"
]
}
],
"source": [
"# Create networks.\n",
"policy_network, critic_network = make_networks_dmpo(action_spec)\n",
"\n",
"# Create agent.\n",
"agent = DistributionalMPO(environment_spec=env_spec,\n",
" policy_network=policy_network,\n",
" critic_network=critic_network,\n",
" observation_network=tf2_utils.batch_concat,\n",
" policy_optimizer=policy_optimizer,\n",
" critic_optimizer=critic_optimizer,\n",
" logger=learner_logger,\n",
" checkpoint=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"## DDPG agent"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {}
},
"outputs": [],
"source": [
"# Create networks.\n",
"policy_network, critic_network = make_networks_ddpg(action_spec)\n",
"\n",
"# Create agent.\n",
"agent = DDPG(environment_spec=env_spec,\n",
" policy_network=policy_network,\n",
" critic_network=critic_network,\n",
" observation_network= tf2_utils.batch_concat, # Identity Op.\n",
" logger=learner_logger,\n",
" checkpoint=checkpoint)"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {}
},
"source": [
"---\n",
"# Good luck :)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"include_colab_link": true,
"name": "robolympics",
"provenance": [],
"toc_visible": true
},
"kernel": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}