Add New Environment into EnvPool

To add a new environment in C++ that EnvPool will parallelly run, we provide a developer interface in envpool/core/env.h.

In the following example, we will create an environment CartPole. It is the same version as OpenAI gym.

The full implementation is in Pull Request 25. Let’s go through the details step by step!

Setup File Structure

The first thing is to fork the project and add the new environment in the envpool folder, i.e., create a classic_control folder under envpool/:

cd envpool
mkdir -p classic_control

Here is the typical file structure:

$ tree classic_control
├── cartpole.h

and their functionalities:

  • to make this directory a python package;

  • BUILD: to indicate the file dependency (because we use Bazel to manage this project);

  • cartpole.h: the CartPole environment;

  • pack via pybind11;

  • a simple unit-test to check if we implement correctly;

  • register CartPole-v0 and CartPole-v1 so that we can use envpool.make("CartPole-v0") to create an environment.

Implement CartPole Environment in cartpole.h

First, include the core header files:

#include "envpool/core/async_envpool.h"
#include "envpool/core/env.h"


Next, we need create CartPoleEnvSpec to define the env-specific config, state space, and action space. Create a class CartPoleEnvFns:

// env-specific definition of config and state/action spec
class CartPoleEnvFns {
  static decltype(auto) DefaultConfig() {
    return MakeDict("reward_threshold"_.Bind(195.0));
  template <typename Config>
  static decltype(auto) StateSpec(const Config& conf) {
    float fmax = std::numeric_limits<float>::max();
    return MakeDict("obs"_.Bind(
        Spec<float>({4}, {{-4.8, -fmax, -M_PI / 7.5, -fmax},
                          {4.8, fmax, M_PI / 7.5, fmax}})));
  template <typename Config>
  static decltype(auto) ActionSpec(const Config& conf) {
    // the last argument in Spec is for the range definition
    return MakeDict("action"_.Bind(Spec<int>({-1}, {0, 1})));

// this line will concat common config and common state/action spec
using CartPoleEnvSpec = EnvSpec<CartPoleEnvFns>;
  • DefaultConfig: the default config to create cartpole environment;

  • StateSpec: the state space (including observation and info) definition;

  • ActionSpec: the action space definition.

CartPole is quite a simple environment. The observation is a NumPy array with shape (4,), and the action is discrete [0, 1]. This definition is also available to see on the python side:

>>> import envpool
>>> spec = envpool.make_spec("CartPole-v0")
>>> spec
CartPoleEnvSpec(num_envs=1, batch_size=1, num_threads=0, max_num_players=1, thread_affinity_offset=-1, base_path='envpool', seed=42, gym_reset_return_info=False, max_episode_steps=200, reward_threshold=195.0)

>>> # if we change a config value
>>> env = envpool.make_gym("CartPole-v0", reward_threshold=666)
>>> env
CartPoleGymEnvPool(num_envs=1, batch_size=1, num_threads=0, max_num_players=1, thread_affinity_offset=-1, base_path='envpool', seed=42, gym_reset_return_info=True, max_episode_steps=200, reward_threshold=666.0)

>>> # observation space and action space
>>> env.observation_space
Box([-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38], [4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38], (4,), float32)
>>> env.action_space
>>> env.spec.reward_threshold


When using a string in MakeDict, you should explicitly use std::string. For example,

auto config = MakeDict("path"_.Bind("init_path"));

The type of “path” will be a const char * type instead of std::string, which sometimes causes config["path"_] to be a meaningless string in further usage. Instead, you should change the code as

auto config = MakeDict("path"_.Bind(std::string("init_path")));


The above example shows how to define a discrete action space by specifying the last argument of Spec. Here is another example, if our environment has 6 actions, ranging from 0 to 5:

template <typename Config>
static decltype(auto) ActionSpec(const Config& conf) {
  return MakeDict("action"_.Bind(Spec<int>({-1}, {0, 5})));
  // or remove -1, no difference in single-player env
  // return MakeDict("action"_.Bind(Spec<int>({}, {0, 5})));

For continuous action space, change the type of Spec to float or double. For example, if the action is a NumPy array with four floats, ranging from -2 to 2:

template <typename Config>
static decltype(auto) ActionSpec(const Config& conf) {
  return MakeDict("action"_.Bind(Spec<float>({-1, 4}, {-2.0, 2.0})));
  // or remove -1, no difference in single-player env
  // return MakeDict("action"_.Bind(Spec<float>({4}, {-2.0, 2.0})));


-1 in Spec is reserved for the number of players. In single-player environment, Spec<int>({-1}) is the same as Spec<int>({}) (empty shape), but in a multi-player environment, empty shape spec will be only a single int value per environment, while the former will be an array with length == #players (can be 0 when all players are dead).


The common config and common state/action spec are defined in env_spec.h.


EnvPool supports the environment that has multiple observations or even nested observations. For example, FetchReach-v1:

>>> import gym
>>> env = gym.make("FetchReach-v1")
>>> e.observation_space
Dict(achieved_goal:Box([-inf ...], [inf ...], (3,), float32), desired_goal:Box([-inf ...], [inf ...], (3,), float32), observation:Box([-inf ...], [inf ...], (10,), float32))
>>> env.reset()
>>> env.step([0, 0, 0, 0])
({'observation': array([ 1.34185919e+00,  7.49100661e-01,  5.34545376e-01,  0.00000000e+00,
          0.00000000e+00,  2.49364315e-05,  2.35502607e-07, -1.56066826e-04,
          3.22889321e-06, -1.55593223e-06]),
  'achieved_goal': array([1.34185919, 0.74910066, 0.53454538]),
  'desired_goal': array([1.36677977, 0.67090477, 0.60136475])},
 {'is_success': 0.0})

If we want to create such a state spec (including both obs and info), here is the solution:

template <typename Config>
static decltype(auto) StateSpec(const Config& conf) {
  return MakeDict(

The keys start with obs: will be parsed to obs dict, and similarly info: will be parsed to info dict.

For nested observations such as {"obs_a": {"obs_b": 6}}, use . to indicate the hierarchy:

return MakeDict("obs:obs_a.obs_b"_.Bind(Spec<int>({})));

It is the same as ActionSpec. The only difference is: there’s no obs: and info: in action.


In dm_env, keys in Spec that start with either obs: or info: will be merged under timestep.observation.


To create a dynamic shape array (which will be converted into a numpy array with object type), you can use Spec<Container<...>>, e.g.:



Now we are going to create a class CartPoleEnv that inherits Env.

We have already defined three types Spec, State and Action in Env class for convenience, which follows the definition of CartPoleEnvSpec.

The following functions are required to override:

  • constructor, in this case it is CartPoleEnv(const Spec& spec, int env_id); you can use spec.config["max_episode_steps"_] to extract the value from config;

  • bool IsDone(): return a boolean that indicate whether the current episode is finished or not;

  • void Reset(): perform one env.reset();

  • void Step(const Action& action): perform one env.step(action).

The reference implementation is in envpool/classic_control/cartpole.h.

Array Read/Write

State and Action are dict-style data structures for easier prototyping. All values in these dictionaries are with type Array, which mimic the functionality of a multi-dimensional array.

To extract value from action in CartPoleEnv:

// auto convert the first element in action["action"_]
int act = action["action"_];
// for continuous action space, e.g.
// float act2 = action["action"_][2];

If the state/action contains several keys and each element is a multi-dimensional array, e.g., an image, there are three ways to deal with array read/write:

uint8_t *ptr = static_cast<uint8_t *>(state["obs"_].data());

for (int i = 0; i < 4; ++i) {
  for (int j = 0; j < 84; ++j) {
    for (int k = 0; k < 84; ++k) {
      // 1. use []
      state["obs"_][i][j][k] = ...
      // 2. use (), faster than 1
      state["obs"_](i, j, k) = ...
      // 3. use raw pointer
      ptr[i * 84 * 84 + j * 84 + k] = ...

If one of the array for state is dynamic-shaped:

Container<int>& dyn = state["obs:dyn"_][i];
// new spec
auto dyn_spec = ::Spec<int>({env_id_ + 1, spec_.config["state_num"_]});
// use this spec to create an array
auto* array = new TArray<int>(dyn_spec);
// perform some normal array writing
// finally pass it to dynamic array

Allocate State in Reset and Step

EnvPool has carefully designed the data movement to achieve zero-copy with the lowest overhead. We create a simple API to make it be more user-friendly.

At the end of Reset and Step function, you need to call Allocate method to allocate state for writing. For example, in CartPoleEnv:

State state = Allocate();
state["obs"_][0] = static_cast<float>(x_);
state["obs"_][1] = static_cast<float>(x_dot_);
state["obs"_][2] = static_cast<float>(theta_);
state["obs"_][3] = static_cast<float>(theta_dot_);
state["reward"_] = 1.0f;

// here is a buggy usage because x_ is float64 and state["obs"_] is float32
// state["obs"_][0] = x_;

You do not pass this state to any other functions or return. Instead, AsyncEnvPool will automatically process the data and pack it to the python interface.


For multi-player environments, you need to allocate state with an extra argument player_num. For example, if the state spec is:

template <typename Config>
static decltype(auto) StateSpec(const Config& conf) {
  return MakeDict(
    "obs:players.obs"_.Bind(Spec<uint8_t>({-1, 4, 84, 84})),
    "obs:players.location"_.Bind(Spec<uint8_t>({-1, 2})),
    "info:bla"_.Bind(Spec<float>({2, 3, 3})),

By calling auto state = Allocate(10), the state would be like:

state["obs:players.obs"_];      // shape: (10, 4, 84, 84)
state["obs:players.location"];  // shape: (10, 2)
state[""];   // shape: (10,)
state["info:player_num"];       // shape: (), only one element
state["info:bla"];              // shape: (2, 3, 3)
state["info:list"];             // shape: (10,) with dtype=object


Please make sure the types are correct. Assigning int to a float array or assigning double to an uint64_t array will not generate any compilation error, but in the actual runtime, the data is wrong. Please use static_cast to convert the type correctly.


After creating CartPoleEnv, just one more line we can get CartPoleEnvPool:

using CartPoleEnvPool = AsyncEnvPool<CartPoleEnv>;



Please do not use the pseudo-random number by rand() % MAX. Instead, use random number distributions to generate thread-safe deterministic pseudo-random numbers. std::mt19937 generator has already been defined as gen_ (link).


ENVPOOL_TEST is a test-time macro. If you want a piece of C++ code only available during unit test:

    fprintf(stderr, "here\n");
    LOG(INFO) << "another error log print method.";

Generate Dynamic Linked .so File and Instantiate in Python

We use pybind11 to let python interface use this C++ code. We have already wrapped this interface, and you need to add only a few lines to make it work:

#include "envpool/classic_control/cartpole.h"
#include "envpool/core/py_envpool.h"

// generate python-side (raw) CartPoleEnvSpec
using CartPoleEnvSpec = PyEnvSpec<classic_control::CartPoleEnvSpec>;
// generate python-side (raw) CartPoleEnvPool
using CartPoleEnvPool = PyEnvPool<classic_control::CartPoleEnvPool>;

// generate
PYBIND11_MODULE(classic_control_envpool, m) {
  REGISTER(m, CartPoleEnvSpec, CartPoleEnvPool)

After that, you can import _CartPoleEnvSpec and _CartPoleEnvPool from

The next step is to apply python-side wrapper (gym/dm_env APIs) to raw classes. In envpool/classic_control/, use py_env function to instantiate CartPoleEnvSpec, CartPoleDMEnvPool, CartPoleGymEnvPool and CartPoleGymnasiumEnvPool.

from envpool.python.api import py_env

from .classic_control_envpool import _CartPoleEnvPool, _CartPoleEnvSpec

) = py_env(_CartPoleEnvSpec, _CartPoleEnvPool)

__all__ = [

Register CartPole-v0/1 in EnvPool

To register a task in EnvPool, you need to call register function in envpool.registration. Here is

from envpool.registration import register



task_id, import_path, spec_cls, dm_cls, gym_cls and gymnasium_cls are required arguments. Other arguments such as max_episode_steps and reward_threshold are env-specific. For example, if someone use envpool.make("CartPole-v1"), the reward_threshold will be set to 475.0 at CartPoleEnvPool initialization.

Finally, it is crucial to let the top-level module import this file. In envpool/, add the following line:

import envpool.classic_control.registration  # noqa: F401

Write Bazel BUILD File

Bazel is a powerful tool to build and test C++-based projects. Python projects can also apply it. Bazel manages all files in EnvPool.

There are some tutorials for Bazel, but for convenience, we only demonstrate the key point here when using Bazel in this project, i.e., how to write BUILD correctly.

Bazel Header

Most of the time, directly include the following things at the top of BUILD:

load("@pip_requirements//:requirements.bzl", "requirement")
load("@pybind11_bazel//:build_defs.bzl", "pybind_extension")

package(default_visibility = ["//visibility:public"])

Types of Rules

  • cc_library: C++ header file *.h, usually for environment definition. Required fields: name, hdrs;

  • cc_test: C++ source file *.cc for running C++ unit tests. Required fields: name, srcs;

  • pybind_extension: C++ source file *.cc to generate .so file with {name}.so. Required fields: name, srcs;

  • py_library: Python library file *.py. Required fields: name, srcs;

  • py_test: Python file *.py for running Python unit tests. Required fields: name, srcs.

All of the above declarations can have deps and data fields, which explicitly specify the dependencies of either a Bazel BUILD rule or a third-party data. We will explain deps in the next section.

If you are looking for other functionalities like gen_rules, please refer to Third-party Dependencies.


Let’s first take a look at BUILD file in classic_control:

load("@pip_requirements//:requirements.bzl", "requirement")
load("@pybind11_bazel//:build_defs.bzl", "pybind_extension")

package(default_visibility = ["//visibility:public"])

    name = "cartpole",
    hdrs = ["cartpole.h"],
    deps = [

    name = "classic_control_envpool",
    srcs = [
    deps = [

    name = "classic_control",
    srcs = [""],
    data = [""],
    deps = ["//envpool/python:api"],

    name = "classic_control_registration",
    srcs = [""],
    deps = [

    name = "classic_control_test",
    srcs = [""],
    deps = [

We have several ways for dependency declaration:

  1. use relative path: :cartpole points to first item (cartpole cc_library);

  2. use absolute path: //envpool/core:async_envpool points to async_envpool under envpool/core;

  3. python dependency: requirement("numpy") means this file use NumPy as runtime dependencies;

  4. third-party dependency (not shown above): will explain in the next section.

And don’t forget to modify the top-level Bazel BUILD dependency:

     name = "entry",
     srcs = [""],
     deps = [
+        "//envpool/classic_control:classic_control_registration",

     name = "envpool",
     srcs = [""],
     deps = [
+        "//envpool/classic_control",

Also, pay attention to check if .so file is packed into .whl successfully. In setup.cfg:

 envpool = atari/*.so
+    classic_control/*.so

Now you can run envpool.make("CartPole-v0") by re-installing EnvPool:

# generate .whl file
make bazel-build
# install .whl
pip install dist/envpool-<version>-*.whl


To test whether the BUILD file is correct for Bazel to compile:

bazel build //envpool/classic_control --config=debug

This command will automatically display the details of the compilation and help make your life easier.

Third-party Dependencies

CartPole environment is so simple that there are no third-party dependencies. However, it is often the case to include some third-party dependencies for a more complex environment.

For third-party Python dependency, for instance, if we want to add tianshou as test dependency, in third_party/pip_requirements/requirements.txt:


If we want to add tianshou as a build dependency, in setup.cfg:

 packages = find:
 python_requires = >=3.7
 install_requires =
+    tianshou

As for source-code dependency, for example, if we want to download ThreadPool and use it in //envpool/core:async_envpool, here are the steps to follow:

  1. add download item for ThreadPool in envpool/workspace0.bzl:

    name = "threadpool",
    sha256 = "18854bb7ecc1fc9d7dda9c798a1ef0c81c2dd331d730c76c75f648189fa0c20f",
    strip_prefix = "ThreadPool-9a42ec1329f259a5f4881a291db1dcb8f2ad9040",
    urls = [
    build_file = "//third_party/threadpool:threadpool.BUILD",

Here is the reference documentation for http_archive.

  1. add ThreadPool into third_party/:

mkdir -p third_party/threadpool
touch third_party/threadpool/BUILD
touch third_party/threadpool/threadpool.BUILD

leave BUILD empty, and add the following rules in threadpool.BUILD:

package(default_visibility = ["//visibility:public"])

    name = "threadpool",
    hdrs = ["ThreadPool.h"],

It says ThreadPool.h is exposed on the top level of “threadpool” namespace.

  1. modify Bazel build rules of async_envpool:

     name = "async_envpool",
     hdrs = ["async_envpool.h"],
     deps = [
+        "@threadpool",

The dependency string format is @<package> or @<package>//:<name>.

For genrule() and data = [...], please refer to Bazel official documentation or Atari BUILD example.

Add Unit Test for CartPoleEnv

It is highly encouraged to write unit tests to ensure the correctness of the new environment. You can write both Python and C++ tests.

C++ Env Tests

We use GoogleTest to run C++ unit tests. You can reach out to Google Test official documentation to see how to use it.

To enable GoogleTest, you need to modify the corresponding Bazel BUILD rule:

     name = "atari_env_test",
     srcs = [""],
     deps = [
+        "@com_google_googletest//:gtest_main",

Python Env Tests

We use Abseil test to run Python unit tests. To enable, you need to modify the corresponding Bazel BUILD rule:

     name = "classic_control_test",
     srcs = [""],
     deps = [
+        requirement("absl-py"),

Make Tests

You can add a test in envpool/ to see if the environment can be successfully created.