Reinforcement learning (RL) has rapidly emerged as one of the most promising and disruptive techniques in artificial intelligence. In this comprehensive guide, we‘ll explore what reinforcement learning is, trace its history, dive into how it works, review current and future applications across industries, spotlight recent advances, and discuss challenges still to be addressed.
A Brief History of Reinforcement Learning
While modern reinforcement learning leverages deep neural networks for unprecedented results, the foundations trace back decades:
- 1950s: Breakthroughs in understanding the psychology of animal learning and decision-making under uncertainty
- 1980s: Formalization of reinforcement learning as a computational technique centered on Markov decision processes
- 1990s-2000s: RL applied successfully in domains like helicopters, elevators, and robotics
- 2012-2015: Deep Q Networks (DQN) powered superhuman play across Atari games solely from pixel inputs
- 2016-present: AlphaGo and AlphaStar defeat world champions in Go and Starcraft II, Protein folding breakthroughs, major investments and industry adoption
Key pioneers behind early reinforcement learning research include Richard Sutton, Andrew Barto, Ronald Williams, Leslie Kaelbling, and Christopher Watkins. Their fundamental work connecting dynamic programming, supervised learning, and behaviorist psychology built the foundation for today‘s deep reinforcement learning explosion.
With breakthrough machine learning capabilities, increased computational resources, and multidisciplinary perspectives now propelling the field forward faster than ever, reinforcement learning sits poised as a transformative technology for industry and research alike.
How Reinforcement Learning Works
Every reinforcement learning system consists of an agent interacting with an environment over a series of discrete time steps:
- The agent observes the current state
- Selects an action to perform
- Receives the next state and a reward
The reward provides a scalar feedback signal that indicates how good or bad the action turned out. The agent aims to maximize cumulative long-term reward through its sequence decisions by learning a policy: a mapping from states to action probabilities.
Solving this interactive sequential decision problem requires optimizing a tradeoff between exploration (gathering more information) and exploitation (maximizing reward with current info). Key categories of methods for addressing RL problems include:
- Value-based: Estimate expected long-term value (return) for choosing actions in states using techniques like dynamic programming or deep Q-learning networks (DQN). High value informs good decisions.
- Policy-based: Directly learn the policy by optimizing parametrized action distributions to maximize reward via likelihood ratio methods like REINFORCE or proximal policy optimization (PPO).
- Actor-critic: Combine value and policy-based learning in an actor-critic architecture, using the critic‘s value estimates to shape the actor‘s policy learning. Methods like A2C, A3C and SAC take this approach.
Let‘s break down a visualization of the agent-environment reinforcement learning framework:
At each time step:
- The agent selects an action based on the policy and current state
- The environment transitions to a next state and returns a reward
- The policy incorporates this experience to improve action choices
By accumulating extensive experience through ongoing interaction, reinforcement learning agents can master sophisticated behaviors, optimizing unique reward signals tailored to the problem.
While Markov Decision Processes (MDPs) underlie many RL formulations thanks to their mathematical tractionability, reinforcement learning methods uniquely extend to handle partial observability, stochasticity, and alternative information models common in real-world environments. This flexibility fuels many practical applications.
Applications of Reinforcement Learning
Let‘s explore some of the most prominent and promising application domains for reinforcement learning:
Robotics
Reinforcement learning delivers an intuitive fit for robotics given the need for dynamic motor control under challenging physics constraints. By maximizing a custom reward function based on factors like achieving desired trajectories, maintaining stability, minimizing energy usage, and avoiding collisions, autonomous control policies emerge even for unstable systems like bipedal locomotion.
In one impressive example, researchers at UC Berkeley employed reinforcement learning to train a robotic hand to smoothly spin a pen over complex trajectories using only a sparse binary reward function. By combining model-based offline data and online fine-tuning, they bypassed any need for explicit human demonstrations. Such advances inch closer toward generalizable robotic manipulation.
Industry leaders have taken notice as well. Google Brain robotics researchers are using reinforcement learning for robotic grasping among other initiatives. And in simulations, Meta AI can solve a Rubik‘s cube with a robotic hand – spanning perceiving state to controlling actions end-to-end with reinforcement learning. Successes like these highlight the expanding capabilities of RL in robotics.
Games & Simulations
Before cracking protein folding and pivoting to industrial applications, DeepMind cut its teeth using video games as proving grounds for novel reinforcement learning algorithms.
Atari and board games share useful properties like clearly defined rewards, reproducible environments, and quick iteration cycles that facilitated rapid testing. In 2015, a Deep Q Network (DQN) achieved superhuman performance across a suite of 49 Atari games using only raw pixels and scores as input. These agents learned behaviors from scratch merely by playing – no demonstrations or game-specific optimizations.
Multiplayer competitive games also played a key role as dynamic testbeds for cutting edge reinforcement learning research. OpenAI‘s Dota 2 bot defeated 99.4% of human players after months of self-play. DeepMind‘s AlphaStar similarly reached Grandmaster level at the strategy game Starcraft II through population based training.
Beyond games, photo-realistic simulations like AI2Thor provide interactive environments for household task training. While games served as an important stepping stone for showcasing algorithm potential, the next frontier tackles impactful real-world problems.
Resource Management
Managing computing infrastructure, energy grids, vehicular traffic networks, and supply chains represents a class of mission-critical domains that can be formulated as optimal sequential decision making under uncertainty.
Taking actions like routing traffic, turning servers on and off, or placing orders affect operational costs and constraints across time. Deep reinforcement learning demonstrates increasing traction for automating these complex resource allocation tasks:
- Baidu reduced data center energy usage over 6% by using deep RL for cooling system control optimization research.
- DeepMind cut Google data center energy by 40% with a novel RL architecture tailored for cooling efficiencynews.
- RL continuously adapts bidding strategy in online auctions to maximize profits as demonstrated by this study.
Ongoing advances around simulation, distributed training, and batch constrained RL widen the range of resource allocation domains amenable to data-driven optimization through reinforcement learning.
Reinforcement Learning in Industry
While DeepMind‘s AlphaGo received widespread public attention as a landmark demonstration of RL capabilities, many companies actively develop real-world applications:
Waymo leverages deep reinforcement learning for tuning behaviors across driving scenarios to minimize human intervention required for their autonomous vehicles. Engineers define customize rewards based on progress towards destination, traffic law conformance, and ride smoothness. By optimizing these signals through simulated and real-world driving experience, Waymo‘s self-driving technology gets closer to performance exceeding human drivers.
Unity offers the Unity ML-Agents toolkit enabling game developers to integrate state-of-the-art reinforcement learning into 3D multi-agent simulations. Home to the world‘s leading real-time engine for interactive content, Unity wants customers leveraging data-driven decisions and procedural autonomy in simulations ranging from architectural design to autonomous vehicle validation.
Microsoft researchers utilize deep reinforcement learning across areas like optimizing machine translation quality, adaptive cloud resource provisioning, and automated machine learning. Products like Microsoft‘s Azure Machine Learning service plan to offer managed reinforced learning model development pipelines to lower barriers for enterprise adoption.
Amazon provides Amazon SageMaker RL forPain reinforcement learning model development, distributed training, automatic tuning, and industry-specific prebuilt environments hosted on AWS infrastructure. Thousands of customers now access RL through SageMaker according to AWS.
In addition, established companies and well-funded startups now recruit aggressively for technical roles spanning research and applied machine learning engineering in reinforcement learning.
Job Market for Reinforcement Learning
The thriving job market continues expanding around reinforcement learning and adjacent areas as companies race to capitalize on recent innovations:
- Reinforcement learning engineer job postings on LinkedIn grew over 450% from 2017 to 2021 based on ThinkAutomation analysis.
- Median salary above $250k for deep reinforcement learning engineers with 5+ years experience according to recruiting firm Genesis Global report.
- Leading hiring companies include Tesla, Apple, Nvidia, Google DeepMind, Zoox, Covariant, Waymo, Uber, and various quant hedge funds.
- Most in-demand skills span Python, TensorFlow/PyTorch, ML systems design, Linux/cloud platforms, simulations (MuJoCo, Gym), and optimization mathematics background.
With cutting edge research migrating into products and profits, reinforcement learning talent now commands premium compensation for those with specialized expertise. Both MLE roles applying RL and PhD researchers pushing boundaries report abundant positions available.
Recent Advances in Reinforcement Learning
While reinforcement learning research began in earnest in the 1990s, combining deep neural networks with RL to enable breakthrough applications has taken off in just the last decade. Let‘s discuss some of those key advances:
Deep Q-Networks
In 2013, DeepMind researchers demonstrated that deep convolutional neural networks could approximate action-value functions in reinforcement learning problems. This launched a new subfield of deep reinforcement learning. Follow-on work showcased how deep Q-learning methods could achieve superhuman performance on Atari 2600 games by learning to interpret raw pixel inputs.
Proximal Policy Optimization
Data efficiency challenges plagued many on earlier policy gradient reinforcement learning algorithms. In 2017, OpenAI introduced Proximal Policy Optimization (PPO). PPO‘s clipped surrogate objective and adaptive batch size adjustment stabilized training to reach state-of-the-art results using fewer samples across diverse continuous control benchmarks.
Massive-Scale Distributed RL
Distributing reinforcement learning training across hundreds to thousands of machines enabled breakthrough results by increasing parallel sample collection. Gorila DQN achieved first superhuman performance on the multidimensional Atari game Frostbite through partitioned replay buffers and parallel acting work. AlphaStar similarly required population based training.
Simulation and Transfer
Physics simulators like MuJoCo now see widespread use for safe, scalable reinforcement learning before deployment to real robots. State representation learning methods enable policies trained purely in simulation to transfer successfully to complex real-world environments by aligning input feature spaces.
Integrating Reconstruction and Control
Model-based reinforcement learning methods leverage predictive models of system dynamics learned from offline datasets to supplement online learning. World Models took this approach by chaining latent state inference, MPC controllers, and vision generators to master visually complex regimes from images paper.
See the chart below for a timeline view highlighting recent reinforcement learning advances:
Ongoing progress across distributed systems, simulation tools, compute infrastructure, model-based techniques, and more continue lowering barriers for applying reinforcement learning successfully. However, challenges still stand in the way of broader adoption.
Challenges Facing Reinforcement Learning
While reinforcement learning enables breakthrough capabilities in research environments, real-world application introduces several key challenges:
Sample Efficiency
The trial-and-error nature of RL generally requires extensive environmental interactions for agents to discover effective policies. State-of-the-art algorithms often utilize hundreds of years of simulated experience. Reducing this sample complexity would smooth adoption. Offline RL provides one route by repurposing previously collected samples more efficiently.
Safe Exploration
Undirected exploration risks unacceptable failures in application areas like healthcare, finance, and transportation. Incorporating safety constraints into training remains an open area needing advancement before RL sees trust for high-stakes decisions. Approaches like reward penalization, scenario specific simulators, and human oversight help mitigate risk currently.
Explainability
With deep neural network representations underlying many modern RL algorithms, agent policies often operate as black boxes unable to explain why certain actions get chosen. Lack of transparency around model behavior hinders monitoring, troubleshooting, and compliance in sensitive applications. Interpretability improvements would address these concerns.
Engineering Challenges
Even with performant algorithms, applying reinforcement learning successfully requires extensive software infrastructure for scalable distributed training, lifecycle management, and integrated simulators. Lack of talent with this specialized expertise considerably slows adoption relative to more turnkey solutions like supervised learning.
In combination, these factors introduce major obstacles for companies looking to build on raw RL research for real products. However, the technology still holds immense promise if hurdles continue lowering through ongoing innovation.
The Future of Reinforcement Learning
Reinforcement learning has already unlocked transformative capabilities in games, robotics, operations research, finance, and other domains where optimizing sequential decisions unlocks value. However, many more promising applications have yet to be tackled at scale.
As algorithms become more sample efficient, engineering best practices disseminate, and infrastructure expands access to simulation, compute and pipelines, development cycles will continue shortening. This allows translating initial research results into concrete business solutions much quicker – accelerating impact.
We also expect pretrained models, public benchmarks, and foundation libraries to kickstart new applications. Just as BERT embeddings transfer learn across NLP tasks and ResNet provides initialized computer vision backends, reusable RL capabilities can reduce initial investment developing custom agents.
Technologically, combining reinforcement learning with other state-of-the-art techniques – multi-task learning, meta learning, graph networks, speech/vision sensors, etc. – should unlock even more impressive capabilities resulting in augmented intelligence outperforming human experts on long-term reasoning tasks.
The future remains bright for reinforcement learning and many research leaders predict it playing an instrumental role advancing artificial general intelligence across sectors. To discuss how RL could transform your business through custom autonomous systems, contact our AI experts today for a free consultation on possibilities.
