Sam Altman (Y Combinator President) – Building Dota Bots That Beat Pros – OpenAI’s Greg Brockman, Szymon Sidor, and Sam Altman (Nov 2017)


Chapters

00:00:00 Advances in Artificial Intelligence: Hardware, Engineering, and Applications
00:09:42 From Lua to Python: Building a Dota Environment for Machine Learning
00:13:52 Behavioral Cloning and Reinforcement Learning in Game Development
00:18:25 Development and Journey of Dota 5.5 Bot
00:26:11 OpenAI's Dota 2 Bot Learns and Adapts to Pro Player
00:31:52 Crafting Adaptive AI Strategies to Overcome Novel Challenges in E-sports
00:35:22 Development of AlphaStar: Journey of Innovation and Adaptation
00:38:12 Unexpected Strategies in AI: Baiting, Psychological Effects, and Bot Fixes
00:43:19 Overnight Coding Efforts: The Road to Dota 2 Victory
00:45:43 How Humans and Bots Collaborate in Competitive Gaming
00:49:29 Fine-tuning Professional Gamers via AI
00:52:48 Non-Technical Skills and AI Startups

Abstract

The Future of AI: Revolutionizing Performance, Understanding Limits, and Blazing New Trails

The rapid evolution of artificial intelligence (AI) is marked by groundbreaking developments in hardware, underexplored research areas, and innovative applications in gaming and real-world challenges. Key advancements in hardware are setting the stage for AI models to exhibit qualitatively different behaviors, with specialized architectures mirroring the brain’s efficiency. Concurrently, a renewed focus on understanding and fine-tuning existing AI methods promises substantial breakthroughs. OpenAI’s foray into the Dota 2 initiative exemplifies this trend, blending engineering prowess with machine learning science to push the boundaries of AI capabilities. This comprehensive overview delves into the nuances of these advancements, shedding light on the intricacies of AI development and its profound implications for society.

Hardware Advancements in AI

The future of AI hardware is poised to unleash unprecedented speed and efficiency. Innovations are emerging in the form of specialized hardware that mimics the brain’s architecture, enabling parallel processing and localized memory storage. This leap forward is expected to revolutionize AI performance, allowing for more complex and nuanced AI behaviors.

Future Advancements

In the realm of neural networks, future hardware advancements are expected to significantly boost the capabilities of these applications. We anticipate seeing unsupervised learning models that master state-of-the-art sentiment analysis classifiers simply by predicting the next character in vast sequences, like Amazon reviews. Moreover, such advancements promise to equip AI models with the ability to process language and images with human-like speed and accuracy. This opens exciting new frontiers for applications in natural language processing, computer vision, and autonomous driving.

The Untapped Potential of AI Research

AI research is now focusing on a deeper understanding of current methodologies and their limitations. Often overlooked basic problems, like classification, hold the key to significant advancements. The refinement of existing algorithms for specific tasks is also seen as a potential goldmine for improving AI efficiency and effectiveness.

Underexplored Areas

There’s a recognized need for research into the understanding of existing methods and their limits. For instance, in deep learning, it was once believed that parallelizing computation required using as small batches as possible. However, recent research by Facebook has contradicted this, showing that larger batch sizes can be used for image classification, resulting in faster task completion. Additionally, there is a pressing need for more robust and reliable AI algorithms, as current systems are often brittle and prone to errors, which can lead to serious consequences in practical applications.

AI’s Practical Application: The Case of Dota 2

OpenAI’s Dota 2 project is a prime example of AI’s application in complex tasks. The choice of Dota 2, influenced by factors like Linux compatibility and a supportive community, provided an ideal platform for AI experimentation. The development involved a deep understanding of the game APIs, transitioning from Lua to Python, and overcoming various technical challenges to create a dynamic AI environment.

Practical Applications

Dota 2 was chosen strategically for AI research because of its Linux compatibility, game API, and large community support. The game’s open and hackable nature, along with Valve’s support, were also crucial factors. The project began with a scripted bot designed by Rafal, which was later replaced by a reinforcement learning-based bot that quickly outperformed its predecessor. Greg Brockman’s contribution, focusing on improving creep blocking through a model that learned intent behind actions, not just mimicking observed behavior, was a breakthrough. This model’s creep blocking skills were notably impressive, ranking among the best in the Dota community.

Behavioral Cloning and Reinforcement Learning

Greg Brockman and Szymon Sidor’s journey in the realms of behavioral cloning and reinforcement learning epitomizes the iterative nature of AI development. Their work, which involved training a bot to mimic expert players and evolve through self-play, emphasizes the crucial role of feedback in shaping AI behavior.

Engineering and Machine Learning Science

Engineering is a pivotal component in AI projects, as evidenced by the Dota 2 project where most work was engineering-related. Effective engineering can immediately make a significant impact in AI. The team’s approach was centered on planning experiments and evaluating outcomes, rather than adhering to rigid milestones. This flexible methodology fostered iterative learning and adaptation, crucial for the project’s success.

The Pursuit of Progress: OpenAI’s Dota 2 Project

OpenAI’s ambitious Dota 2 project aimed to develop a bot capable of defeating professional players. The project transitioned from scripted bots to reinforcement learning-powered ones, resulting in rapid performance enhancements. A key focus was the bot’s true skill rating, a critical progress measure. The project reached its zenith at the International tournament, where the AI bot competed against top players, constantly adapting and evolving its strategies.

From Day to Day

The Dota 2 bot showcased remarkable daily improvements, with each new version outperforming the previous day’s professional player. Its parameters were updated each morning, enabling continuous enhancement of its capabilities.

Special Event with Dendi

A highlight of the project was a special event where the bot was tested against Dendi, a legendary Dota 2 player. The bot’s prowess was also assessed against other professional players present at the event.

Time Constraints and Infrastructure Challenges

As the Dota 2 competition neared, OpenAI grappled with stringent time constraints. The bot’s training was extensive and left no room for errors or last-minute fixes.

Unexpected Match against Top Players

An unanticipated match was scheduled against Arteezy and Sumail, two of the world’s top Dota 2 players. Concerns arose when the bot, during testing, was easily defeated by a semi-pro player, indicating potential weaknesses in its performance.

Insights from AI Battles

The Dota 2 project offered valuable insights into AI development. Notably, the bot’s exposure to new strategies like the wand build significantly enhanced its capabilities. The project emphasized the importance of feature engineering and model optimization, with a focus on complex strategies over basic tasks.

Game Results

The Dota 2 bot first defeated three professional gamers and an analyst 3-0 in Blitz matches. It then faced another pro, Pycat, winning the first two games but losing the third. This loss was attributed to the bot’s unfamiliarity with Pycat’s early wand build strategy.

Bot’s Learning Curve

Following its initial defeat, the bot quickly adapted, learning to counter new strategies and item builds. Its subsequent 3-0 victory against another pro demonstrated its rapid learning and adaptability.

Exploiting the Bot

Professional players eventually found exploits and weaknesses in the bot’s strategies, underscoring the significance of the bot’s learning environment.

Experimentation and Time Constraints

The development of OpenAI’s Dota 2 bot was a continuous race against time, marked by daily iterations and adjustments. The team faced various challenges, from identifying flaws in the bot’s strategy to preparing for high-stakes matches against top-tier players.

Infrastructure and Tooling

Infrastructure and tooling are critical in AI projects. OpenAI’s use of Kubernetes for monitoring and managing the infrastructure exemplifies this. The training process was analogous to teaching a human, where incremental adjustments led to performance improvements, allowing human programmers to focus on high-level strategies.

The Bot’s Learning Curve and Human Adaptation

The bot’s ability to develop strategies like baiting, impacting human players psychologically, was a notable aspect of the project. Some players consistently beat the bot, showcasing the dynamic interplay between AI and human strategy.

Changing the Bot’s Behavior

The OpenAI team could alter the bot’s behavior with specific programming tweaks, ranging from adding new item builds to adjusting its decision-making logic.

Learning to Bait

The bot unexpectedly developed a baiting strategy, pretending weakness and then attacking suddenly. While not optimal against skilled opponents, this strategy was effective against the general bot population.

Franken-bot Creation

Approaching the deadline, the team combined an older bot, proficient in early game strategies, with a new bot excelling in late-game strategies, resulting in a Franken-bot just in time for the pro-player showcase.

Fixing the Baiting Strategy

The team debated whether to abandon the bot or allow it to train longer to learn a counter-strategy to baiting. They opted for additional training, resulting in significant improvements and victories against pro-player Arteezy.

Preparing for Sumail’s Challenge

In preparation for facing pro-player Sumail, the team spent the last day relaxing and updating the network parameters, allowing the bot to train overnight in contrast to typical engineering deadlines.

The Human Side of AI Development

The Dota 2 project required a diverse skill set from the OpenAI team, including knowledge in distributed systems, linear algebra, and basic statistics. Non-technical skills like humility and a passion for AI research were also emphasized.

Collaborative Teamwork in Bot Development

Teamwork played a crucial role in the bot’s development, with members working tirelessly to address production outages and make rapid improvements.

Challenges and Experiences of Bot Development

Developing the bot involved long days of meetings, observing the bot’s performance, and understanding its behavior to make effective improvements.

AI’s Broader Impact

AI’s transformative potential reaches beyond gaming, impacting society at large. Ethical implications must be considered, as AI capabilities continue to advance.

Recap of the Night Before the Dota 2 Match with Samael

The night before the Dota 2 match with Samael was intense, with the team working till midnight to implement changes. A call with Azure at 3am to raise machine limits and a deployment process that lasted until morning highlighted the team’s dedication. The experiment was up and running by 11am, allowing some much-needed rest before waking up at 4pm with over 24 hours left for training before the match.

Game Schedule

The match schedule was rigorous. Monday saw the bot’s first set of games and a loss. Tuesday involved model surgery and resumed play at 11am. By Wednesday, the bot played Arteezy at 4pm and continued training. Thursday marked the final day of changes, culminating in a match against Sumail.

Supplemental Update

Insights from Observing Bot Play

Syho, a programming competition expert, spent time analyzing the bot’s micro-decisions. This human-like approach offered insights into the bot’s creative strategies and the rationale behind its choices.

Balancing Observability and Behavioral Understanding

In machine learning systems, understanding behavior at its core is crucial, as not everything is directly observable, unlike traditional system designs that focus on observability and metrics.

Unexpected Strategies Discovered by the Bot

The bot’s discovery of innovative strategies like “baiting” surprised developers and demonstrated its ability to deviate from expected patterns. It also taught semi-pro players new strategies effective against human opponents.

Bot’s Performance against Professionals

The bot maintained an undefeated streak against pro player Sumail in several matches. As humans played more against the bot, they improved their win rate, suggesting that the bot’s strategies could be learned and countered.

Professional Players and Bot Mastery

Professional player Arteezy, through extensive gameplay with the bot, achieved a skill level comparable to the AI.

Impact on Playstyle

Playing against the bot led Arteezy to focus more on fundamental game aspects, influenced by the bot’s proficiency.

Improving Human Playstyle

The interaction between humans and AI bots is revealing new avenues for enhancing human strategies and playstyle in Dota 2.

Essential Skills for OpenAI Work

Key skills for AI development at OpenAI include knowledge of distributed systems and the ability to write bug-free code.

Writing Bug-Free Code

The emphasis on minimal bugs in code is paramount due to the high cost of debugging and its impact on training performance.

Simplicity and Code Optimization

At times, OpenAI prioritizes code simplicity over good engineering practices to minimize potential bug-prone areas.

Mathematics Proficiency

A strong grasp of mathematics, particularly linear algebra and basic statistics, is beneficial for those looking to work in AI research.

Technical Skills

Essential technical skills include linear algebra, basic statistics, and the ability to balance engineering discipline with flexibility.

Non-Technical Skills

Humility is crucial in AI research, and non-technical individuals can contribute by participating in ethical discussions and educating themselves about AI advancements.

AI Research Challenges

AI research is complex and demands highly skilled individuals. Video games serve as valuable test beds for AI, with the ultimate goal of applying these advancements to real-world problems and human interactions.

Getting Involved with OpenAI

OpenAI offers opportunities for individuals with diverse technical skills. A PhD in AI is not mandatory for contributing to large-scale reinforcement learning projects.

In summary, the journey of OpenAI’s Dota 2 project, from its inception to the challenges and successes encountered, highlights the multifaceted nature of AI development. The project not only advanced AI technology but also provided significant insights into the interplay between AI and human cognition, strategy, and adaptability. The broader impact of AI in society, coupled with the ethical considerations it entails, underscores the need for continuous research, development, and informed discussions in this rapidly evolving field.


Notes by: Flaneur