rlenvscpp

rlenvscpp is an effort to provide implementations and wrappers of environments suitable for training reinforcement learning agents using C++. In addition, the library provides various utilities such as experiment tracking, representing trajectories via waypoints and simple implementation of popular dynamics such as quadrotor dynamics.

Various RL algorithms using the environments can be found at cuberl.

The documentation for the library can be found here. The following is an example how to use the FrozenLake environment from Gymnasium.

	#include "rlenvs/rlenvs_types_v2.h"
	#include "rlenvs/envs/gymnasium/toy_text/frozen_lake_env.h"
	#include "rlenvs/envs/api_server/apiserver.h"

	#include <iostream>
	#include <string>
	#include <unordered_map>
	#include <any>

	namespace example_1{

	const std::string SERVER_URL = "http://0.0.0.0:8001/api";

	using rlenvscpp::envs::gymnasium::FrozenLake;
	using rlenvscpp::envs::RESTApiServerWrapper;


	void test_frozen_lake(const RESTApiServerWrapper& server){

		FrozenLake<4> env(server);

		std::cout<<"Environame URL: "<<env.get_url()<<std::endl;

		// make the environment
		std::unordered_map<std::string, std::any> options;
		options.insert({"is_slippery", false});
		env.make("v1", options);

		std::cout<<"Is environment created? "<<env.is_created()<<std::endl;
		std::cout<<"Is environment alive? "<<env.is_alive()<<std::endl;
		std::cout<<"Number of valid actions? "<<env.n_actions()<<std::endl;
		std::cout<<"Number of states? "<<env.n_states()<<std::endl;

		// reset the environment
		auto time_step = env.reset(42, std::unordered_map<std::string, std::any>());

		std::cout<<"Reward on reset: "<<time_step.reward()<<std::endl;
		std::cout<<"Observation on reset: "<<time_step.observation()<<std::endl;
		std::cout<<"Is terminal state: "<<time_step.done()<<std::endl;

		//...print the time_step
		std::cout<<time_step<<std::endl;

		// take an action in the environment
		// 2 = RIGHT
		auto new_time_step = env.step(2);

		std::cout<<new_time_step<<std::endl;

		// get the dynamics of the environment for the given state and action
		auto state = 0;
		auto action = 1;
		auto dynamics = env.p(state, action);

		std::cout<<"Dynamics for state="<<state<<" and action="<<action<<std::endl;

		for(auto item:dynamics){

			std::cout<<std::get<0>(item)<<std::endl;
			std::cout<<std::get<1>(item)<<std::endl;
			std::cout<<std::get<2>(item)<<std::endl;
			std::cout<<std::get<3>(item)<<std::endl;
		}
		
		action = env.sample_action();
		new_time_step = env.step(action);

		std::cout<<new_time_step<<std::endl;
		
		// synchronize the environment
		env.sync(std::unordered_map<std::string, std::any>());
		
		auto copy_env = env.make_copy(1);
		copy_env.reset();
		
		std::cout<<"Org env cidx: "<<env.cidx()<<std::endl;
		std::cout<<"Copy env cidx: "<<copy_env.cidx()<<std::endl;
		
		copy_env.close();

		// close the environment
		env.close();

	}

	}

	int main(){

		using namespace example_1;
		
		RESTApiServerWrapper server(SERVER_URL, true);

		std::cout<<"Testing FrozenLake..."<<std::endl;
		example_1::test_frozen_lake(server);
		std::cout<<"===================="<<std::endl;
		return 0;
	}

See also Working with Webots for how to integrate rlenvscpp with Webots. Example Using rlenvscpp with Ray shows how to use Ray with the rlenvscpp.