Oriol Vinyals: DeepMind AlphaStar, StarCraft, and Language | Lex Fridman Podcast #20 | Summary and Q&A

April 29, 2019
Lex Fridman Podcast
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Oriol Vinyals: DeepMind AlphaStar, StarCraft, and Language | Lex Fridman Podcast #20


DeepMind's AlphaStar used deep reinforcement learning to beat top professional players in the game StarCraft, marking a major breakthrough in AI research and gaming.

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Key Insights

  • 🔬 Alpha Star: Spearheaded by Ariane Vinnie Alice, Alpha Star is a deep reinforcement learning agent developed by DeepMind that defeated a top professional player in the game of StarCraft. The project focuses on exploring the challenges of exploration and long-term planning in the real-time strategy game.
  • 🎮 Passion for Video Games: Ariane Vinnie Alice's love for video games, particularly StarCraft, preceded his passion for programming. He played StarCraft professionally in the late 90s and developed skill in playing all three main races (Terran, Protoss, and Zerg), with a preference for Zerg.
  • 🌌 Understanding StarCraft: StarCraft is a real-time strategy game where players compete against each other or AI opponents on a map. It requires gathering resources, building units and buildings, and strategizing to defeat the opponent. The game offers a challenging mix of decision-making, resource management, and partial observability.
  • 🌐 Impact of Gaming: Online gaming, particularly StarCraft and Blizzard's Battle.net, has had a transformative impact on society, bringing diverse individuals together and creating social connections that transcend geographical boundaries.
  • ⚖️ Balancing and Innovation: Blizzard, the creator of StarCraft, continuously works on balancing the game's classes and introducing new elements to keep the gameplay fair and interesting. They also see the potential of artificial intelligence in gaming and are interested in exploring AI's possibilities in game development.
  • 🤖 The Challenges of StarCraft: StarCraft poses several challenges for AI, including the large action space, partial observability, imperfect information, and real-time decision-making. DeepMind's AlphaStar project addresses these challenges by utilizing deep reinforcement learning to train agents.
  • 📊 Representation and Policy: AlphaStar's policy, implemented through a neural network, represents the state of the game through a combination of spatial images and a set of objects representing the units. The architecture used is inspired by approaches in natural language processing, particularly in machine translation.
  • 🏆 Human-Like Play: To achieve human-like play, AlphaStar is initially trained through imitation learning, imitating the actions of human players. As self-play progresses, the policy is refined by experiencing wins and losses, bringing it closer to human-level play.
  • 🚧 Constraints and Precision: To ensure fairness and avoid excessive actions per minute, AlphaStar applies constraints on agent behavior. However, there is ongoing discussion about the optimal level of restrictions and the potential for more innovative approaches.


the following is a conversation with Ariane Vinnie Alice he's a senior research scientist at google deepmind and before that he was a Google brain and Berkeley his research has been cited over 39,000 times he's truly one of the most brilliant and impactful minds in the field of deep learning he's behind some of the biggest papers and ideas and AI i... Read More

Questions & Answers

Q: What is the main challenge in teaching an AI agent to play StarCraft using deep reinforcement learning?

The main challenge is the exploration problem, as the large action space and partial observability make it difficult for the agent to find optimal strategies. The agent must learn through trial and error, which can be time-consuming and require a vast amount of data.

Q: How did the AlphaStar project utilize human replays of StarCraft games?

DeepMind leveraged a large dataset of human replays provided by Blizzard, the creators of StarCraft. This dataset allowed the AI agent to learn from the strategies and actions taken by skilled human players, helping it understand the game dynamics and improve its gameplay.

Q: What are some differences in how humans and AlphaStar perceive the game of StarCraft?

One notable difference is the ability to detect cloaked units in the game. Humans rely on visual cues, such as a shimmer, to spot these units, while AlphaStar can immediately detect their presence based on the game data. Additionally, humans may miss certain details or units, whereas AlphaStar can accurately analyze the entire game state.

Q: How does AlphaStar balance the rate of actions per minute (APM) to make it human-like?

DeepMind used imitation learning to train AlphaStar, where it imitated the actions of human players. By analyzing the actions per minute of professional players, they set certain cutoffs for APM to ensure the agent's behavior aligns with human levels. However, self-play might lead to changes in APM, and DeepMind continues to work on fine-tuning this aspect.

Q: How does AlphaStar choose which race to play in StarCraft?

In the demonstration, AlphaStar played as the Protoss race. Each race in StarCraft has different strengths and tactics. Protoss is known for its advanced technology and expensive yet powerful units. DeepMind's choice of Protoss was likely based on its strategic capabilities and suitability for the AI agent's learning process.

Q: What is the main challenge in teaching an AI agent to play StarCraft using deep reinforcement learning?

The main challenge is the exploration problem, as the large action space and partial observability make it difficult for the agent to find optimal strategies. The agent must learn through trial and error, which can be time-consuming and require a vast amount of data.


In this conversation, Ariane Vinnie Alice, a senior research scientist at Google DeepMind, discusses his work on the AlphaStar project, which created an agent that defeated a top professional player in the game of StarCraft. He talks about his background in programming and love for videogames, his strategies and experiences playing StarCraft, and the history and importance of gaming over the past 20 years. He also dives into the challenges of StarCraft, such as its imperfect information, long-term planning, real-time aspects, and large action space. Ariane explains the architecture and internals of AlphaStar, including how the state of the game is represented, the long-term sequence modeling, and the policy network. He also touches on the differences between how AlphaStar and human players perceive the game.

Questions & Answers

Q: What came first for Ariane Vinnie Alice, a love for programming or a love for videogames?

For Ariane, his love for video games came first. He enjoyed computers and playing with them, but his coding skills were limited. He spent most of his time playing video games, especially StarCraft, which he played passionately and even semi-professionally in the 90s.

Q: How would Ariane describe StarCraft to someone who has never played videogames, especially online games?

Ariane describes StarCraft as a real-time strategy game, similar to chess. It has a board or map where players face off against each other with different units and resources. Unlike chess, in StarCraft, players start with no pieces and have to gather resources, build units, and strategize to attack their opponent. It is a complex and fast-paced game that requires real-time decision-making and skill in both resource management and combat.

Q: What is the societal impact of gaming over the past 20 years?

Ariane believes that gaming, especially online gaming, has had a significant impact on society. He shares his own experience of playing online games and connecting with people from different backgrounds and countries. It has helped him understand and appreciate the diversity of the world. While gaming used to be seen as a niche or strange activity, it is becoming more mainstream, and more people are recognizing its importance and influence.

Q: How was the AlphaStar project initiated, and what were the parameters of the challenge?

Ariane explains that the AlphaStar project started when DeepMind was acquired by Google. He proposed the idea of applying deep reinforcement learning to StarCraft, and after a few years, an opportunity arose when Blizzard reached out to DeepMind to take on the challenge. The goal was to train an AI agent that could beat top professional players. The challenge involved dealing with the game's large action space, imperfect information, and real-time aspects.

Q: What is the hardest aspect of StarCraft for the AI to tackle?

Ariane identifies exploration as the primary challenge in StarCraft. The large action space, real-time nature of the game, and the need for strategic planning make exploration crucial for learning and improving. Without any prior knowledge or guidance, exploration becomes difficult. The AI needs to learn and discover effective strategies by taking random actions and then learning from the outcomes. This exploration problem is the hardest aspect for the AI in StarCraft.

Q: How does AlphaStar represent the state of the game and build a policy for decision-making?

AlphaStar represents the state of the game using a mix of spatial images and a list of units. The images capture the zoomed-out view of the map, while the list of units provides detailed information about their properties and positions. The policy, which is a neural network, takes in these representations and predicts the next action to be taken. The long-term sequence modeling is reminiscent of machine translation in natural language processing, where the network predicts the next action given all past observations and actions.

Q: Is there a self-play component in AlphaStar?

Yes, after the initial imitation learning from human replays, AlphaStar incorporates self-play. The initial agent trained from human replays is not as good as the human players it imitates. Through self-play, the agent experiences wins and losses, enabling it to refine its strategy and improve. Self-play is an important aspect in ensuring that the policy understands what it means to win and to continually improve the agent's performance.

Q: Is there a significant difference in how AlphaStar and human players perceive the game?

Yes, there are a few differences in how AlphaStar and human players perceive the game. One notable difference is how AlphaStar can immediately detect cloaked units, which are invisible to human players until they notice a shimmer or distortion. AlphaStar, on the other hand, can perceive a unit entering its field of view as an immediate and precise event. Additionally, the rate of action and precision in AlphaStar are comparable to professional players, although there are still challenges in determining the exact rate and precision that would match human players.

Q: Can AlphaStar be controlled to play at a specific MMR level?

AlphaStar aims to imitate human players of different skill levels. By training the policy on replays from human players of specific MMR levels, AlphaStar learns to mimic their actions and play at a similar skill level. However, there is still work to be done in refining the policy to truly understand what it means to win and achieve human-level performance consistently.

Q: What aspect of StarCraft is the most challenging or annoying according to Ariane?

Ariane considers exploration to be the most challenging aspect of StarCraft. The large action space and real-time nature make exploration necessary for learning effective strategies. AlphaStar needs to explore and discover viable actions by taking random actions and learning from them. This exploration problem, combined with the absence of perfect strategies due to partial observability, poses significant challenges for AI agents in StarCraft.

Summary & Key Takeaways

  • Ariane Vinnie Alice, senior research scientist at DeepMind, spearheaded the team behind AlphaStar, a project that used deep reinforcement learning to defeat top professional players in StarCraft.

  • The project began in 2016 and started with exploring rule-based strategies before moving towards a machine learning approach using neural networks.

  • The team faced challenges such as the exploration problem and representing the game state accurately, but ultimately achieved significant success in training the AI agent.

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