CaiRL A High-Performance Reinforcement Learning Environment Toolkit Per-Arne Andersen

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CaiRL: A High-Performance Reinforcement

Learning Environment Toolkit

Per-Arne Andersen

Department of ICT

University of Agder

Grimstad, Norway

per.andersen@uia.no

Morten Goodwin

Department of ICT

University of Agder

Grimstad, Norway

morten.goodwin@uia.no

Ole-Christoffer Granmo

Department of ICT

University of Agder

Grimstad, Norway

ole.granmo@uia.no

Abstract—This paper addresses the dire need for a platform

that efﬁciently provides a framework for running reinforcement

learning (RL) experiments. We propose the CaiRL Environment

Toolkit as an efﬁcient, compatible, and more sustainable alterna-

tive for training learning agents and propose methods to develop

more efﬁcient environment simulations.

There is an increasing focus on developing sustainable artiﬁcial

intelligence. However, little effort has been made to improve the

efﬁciency of running environment simulations. The most popular

development toolkit for reinforcement learning, OpenAI Gym, is

built using Python, a powerful but slow programming language.

We propose a toolkit written in C++ with the same ﬂexibility level

but works orders of magnitude faster to make up for Python’s

inefﬁciency. This would drastically cut climate emissions.

CaiRL also presents the ﬁrst reinforcement learning toolkit

with a built-in JVM and Flash support for running legacy

ﬂash games for reinforcement learning research. We demonstrate

the effectiveness of CaiRL in the classic control benchmark,

comparing the execution speed to OpenAI Gym. Furthermore,

we illustrate that CaiRL can act as a drop-in replacement for

OpenAI Gym to leverage signiﬁcantly faster training speeds

because of the reduced environment computation time.

Index Terms—Reinforcement Learning, Environments, Sus-

tainable AI

I. INTRODUCTION

Reinforcement Learning (RL) is a machine learning area

concerned with sequential decision-making in real or simu-

lated environments. RL has a solid theoretical background and

shows outstanding capabilities to learn control in unknown

non-stationary state-spaces [1]–[3]. Recent literature demon-

strates that Deep RL can master complex games such as Go

[4], StarCraft II [5], and progressively move towards mas-

tering safe autonomous control [1]. Furthermore, RL has the

potential to contribute to health care for tumor classiﬁcation

[6], ﬁnances [7], and industry-4.0 [8] applications. RL solves

problems iteratively by making decisions while learning from

received feedback signals.

A. Research Gap

However, fundamental challenges limit RL from working

reliably in real-world systems. The ﬁrst issue is that the

exploration-exploitation trade-off is difﬁcult to balance in real-

world systems because it is also a trade-off between suc-

cessful learning and safe learning [1]. The reward-is-enough

hypothesis suggests that RL algorithms can develop general

and multi-attribute intelligence in complex environments by

following a well-deﬁned reward signal. However, the second

challenge is that reward functions that lead to efﬁcient and

safe training in complex environments are difﬁcult to deﬁne

[9]. Given that it is feasible to craft an optimal reward function,

agents could quickly learn to reach the desired behavior but

still require exploration to ﬁnd a good policy. RL also requires

many samples to learn an optimal behavior, making it difﬁcult

for a policy to converge without simulated environments.

While there are efforts to address RL’s safety and sample

efﬁciency concerns, it remains an open question [10]. These

concerns represent challenges for training RL algorithms

climate-efﬁcient in an environmentally responsible manner.

Most RL algorithms require substantial calculations to train

and simulate environments before achieving satisfactory data

to conclude performance measurements. Therefore, current

state-of-the-art methods have a signiﬁcant negative impact on

the climate footprint of machine learning [11].

Because of the difﬁculties mentioned above, a large percent-

age of RL research is conducted in environment simulations.

Learning from a simulation is convenient because it simpliﬁes

quantitative research by allowing agents to freely make deci-

sions that learn from catastrophic occurrences without causing

harm to humans or real systems. Furthermore, simulations can

operate quicker than real-world systems, addressing some of

the issues caused by low sample efﬁciency.

There are substantial efforts in the RL ﬁeld that focus on

improving sample efﬁciency for algorithms but little work on

improving simulation performance through implementation or

awareness. Currently, most environments and simulations in

RL research are integrated, implemented, or used through the

Open AI Gym toolkit. The beneﬁt of using AI Gym is that it

provides a common interface that uniﬁes the API for running

experiments in different environments. There are other such

efforts like Atari 2600 [12], Malmo Project [13], Vizdoom

[14], and DeepMind Lab [15], but there is, to the best of

our knowledge, no toolkit that competes with the environment

diversity seen in AI Gym. AI Gym is written in Python,

an interpreted high-level programming language, leading to

a signiﬁcant performance penalty. At the same time, AI Gym

has substantial traction in the RL research community. Our

concern is that this gradually leads to more RL environments

arXiv:2210.01235v1 [cs.LG] 3 Oct 2022

and problems being implemented in Python. Consequently,

RL experiments may cause unnecessary computing costs and

computation time, which results in a higher carbon emission

footprint [16]. Our concern is further increased by comparing

the number of RL environment implementations in Python

versus other low-level programming languages.

Our contribution addresses this gap by developing an al-

ternative to AI Gym without these adverse side effects by

offering a comparable interface and increasing computational

efﬁciency. As a result, we hope to reduce the carbon emissions

of RL experiments for a more sustainable AI.

B. Contribution Scope

We propose the CaiRL Environment toolkit to ﬁll the gap

of a ﬂexible and high-performance toolkit for running rein-

forcement learning experiments. CaiRL is a C++ interface to

improve setup, development, and execution times. Our toolkit

moves a considerable amount of computation to compile

time, which substantially reduces load times and the run-time

computation requirements for environments implemented in

the toolkit. CaiRL aims to have a near-identical interface to

AI Gym, ensuring that migrating existing codebases requires

minimal effort. As part of the CaiRL toolkit, we present, to

the best of our knowledge, the ﬁrst Adobe Flash compatible

RL interface with support for Actionscript 2 and 3.

Additionally, CaiRL supports environments running in the

Java Virtual Machine (JVM), enabling the toolkit to run Java

seamlessly if porting code to C++ is impractical. Finally,

CaiRL supports the widely used AI Gym toolkit, enabling

existing Python environments to run seamlessly. Our contri-

butions summarize as follows:

1) Implement a more climate-sustainable and efﬁcient ex-

periment execution toolkit for RL research.

2) Contribute novel problems for reinforcement learning

research as part of the CaiRL ecosystem.

3) Empirically demonstrate the performance effectiveness

of CaiRL.

4) Show that our solution effectively reduces the carbon

emission footprint when measuring following the met-

rics in [17].

5) Evaluate the training speed of CaiRL and AI Gym and

empirically verify that improving environment execution

times can substantially reduce the wall-clock time used

to learn RL agents.

C. Paper Organization

In Section 2, we dive into the existing literature on re-

inforcement learning game design and compare the existing

solution to ﬁnd the gap for our research question. Section 3

details reinforcement learning from the perspective of CaiRL

and the problem we aim to solve. Section 4 details the design

choices of CaiRL and provides a thorough justiﬁcation for

design choices. Section 5 presents our empirical ﬁndings of

performance, adoption challenges, and how they are solved,

and ﬁnally compares the interface of the CaiRL framework

to OpenAI Gym (AI Gym). Section 6 presents a brief design

recommendation for developers of new environments aimed

at reinforcement learning research. Finally, we conclude our

work and outline a path forwards for adopting CaiRL.

II. BACKGROUND

A. Reinforcement Learning

Reinforcement Learning is modeled according to a Markov

Decision Process (MDP) described formally by a tuple

(S, A, T, R, γ, s0).Sis the state-space, Ais the action-space,

T:S×A→Sis the transition function, R:S×A→R

is the reward function [18], γis the discount factor, and s0

is starting state. In the context of RL, the agent operates

iteratively until reaching a terminal state, at which time the

program terminates. Q-Learning is an off-policy RL algorithm

and seeks to ﬁnd the best action to take given the current

state. The algorithm operates off a Q-table, an n-dimensional

matrix that follows the shape of state dimensions where the

ﬁnal dimension is the Q-values. Q-Values quantify how good

it is to act aat time t. This work uses Deep Learning

function approximators in place of Q-tables to allow training

in high-dimension domains [19]. This forms the algorithm

Deep Q-Network (DQN), one of the ﬁrst deep learning-

based approaches to RL, and is commonly known for solving

Atari 2600 with superhuman performance [19]. Section V-C

demonstrates that our toolkit signiﬁcantly reduces the run-

time and carbon emission footprint when training DQN in

traditional control environments.

B. Graphics Acceleration

A graphics accelerator or a graphical processing unit (GPU)

intends to execute machine code to produce images stored in

a frame buffer. The machine code instructions are generated

using a rendering unit that communicates with the central

processing unit (CPU) or the GPU. These methods are called

software rendering or hardware rendering, respectively. GPUs

are specialized electronics for calculating graphics with vastly

superior parallelization capabilities to their software counter-

part, the CPU. Therefore, hardware rendering is typically pre-

ferred for computationally heavy rendering workloads. Con-

sequently, it is reasonable to infer that hardware-accelerated

graphics provide the best performance due to their improved

capacity to generate frames quickly. On the other hand, we

note that when the rendering process is relatively basic (e.g.,

2D graphics) and access to the frame buffer is desired, the

expense of moving the frame buffer from GPU memory to

CPU memory dramatically outweighs the beneﬁts. [20]

According to [20], software rendering in modern CPU chips

performs 2-10x faster due to specialized bytecode instructions.

This study concludes that the GPU can render frames faster,

provided that the frame permanently resides in GPU memory.

Having frames in the GPU memory is impractical for machine

learning applications because of the copy between the CPU

and GPU. The authors in [21] propose using Single Instruction

Multiple Data (SIMD) optimizations to improve game perfor-

mance. SIMD extends the CPU instruction set for vectorized

arithmetic to increase instruction throughput. The authors ﬁnd

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CaiRL:AHigh-PerformanceReinforcementLearningEnvironmentToolkitPer-ArneAndersenDepartmentofICTUniversityofAgderGrimstad,Norwayper.andersen@uia.noMortenGoodwinDepartmentofICTUniversityofAgderGrimstad,Norwaymorten.goodwin@uia.noOle-ChristofferGranmoDepartmentofICTUniversityofAgderGrimstad,Norwayole.gra...

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CaiRL A High-Performance Reinforcement Learning Environment Toolkit Per-Arne Andersen

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