OpenAI Five

OpenAI · Beginner ·🎯 Management & AI-Era Leadership ·8y ago

Key Takeaways

OpenAI Five, an AI system, defeats amateur human teams at Dota 2 using reinforcement learning, demonstrating its ability to coordinate and focus as a team of five, with plans to challenge professional teams at the Dota World Championships.

Full Transcript

Skoda is one of the most popular and challenging competitive video games ever playing dota means you have to coordinate and focus as a team of five last year we built a box to defeated the world's best players at the 1v1 mini game this year we want to beat the best proteins at the full game to do this we've built a new AI system which we spend the last few months training it uses a reinforcement learning at a much larger scale to train our BOTS to play together as a team we're now starting to play against amateur teams to test our skill and to our surprise so far we've won our first games against every team we've tested everyone to be everyone I can't do it but to figure out how good our bots really are yes and go to expert to take a look my name is William Lee better known in the DOTA community as blitz let's here with Krissy just gonna give a brief explanation of the game for people that don't know about the game rules are very simple it's kill the enemy team take the buildings every hero has a variety of unique spells they'll be playing a mirror mode where both teams have the exact same heroes here the human team is dealing significant damage to the base is 5 the crystal maiden comes in to defend the barracks she jumps in with blink dagger and shields herself with black King Bar while channeling freezing field to deal massive amounts of damage while being uninterruptible due to her combo she ends up taking up for human players in the 2v5 fight and Gigi's called game humans no longer think they can win they would be absolutely correct so their dev team got absolutely crushed I think is the fastest casting gig of my life then it went into game number two the humans kind of I mean they had time to like think about the game and stuff like that they got crushed even harder and the bots did exactly what I hope for is they own this area of the map you take away two thirds of the map they didn't even touch these two bottom towers and they would be 100% correct in this this is like one of the highest level plays that you can make this side of the map is incredibly hard for the boss to control and so they're just playing this top side and this mid side because they understand that these are the two most important parts to control the game the ability to like intuitively do this is insane doing it one game I can maybe chalk it up to just dumb luck doing it two games in a row flipping the sides means that it's more than just coincidence it took me and I'm fairly reasonably good at the game eight years before I learned some of the strategies I would say it was pretty easy to quantify for me it was about eight years for me to learn the strategies that the bot was intuitively doing to train our BOTS we use reinforcement learning myself play we run the game on over a hundred thousand CPUs and our bots learn from every game they play because dota is so complex to learn even for a single player we created a hyper parameter which we call team spirit the five bots start out completely selfish but tuning this knob tells them to care about their teammates so that they can learn to play together as one unit after seeing the bots win against the test team what's one to challenge it himself he has a higher rating from the test team and we paired him up with the best players in our audience and so we can see blitz about to die here blade says definitely dead yes so now the humans are in trouble truth the team members are are dead with 20 seconds remaining and the bots are about to take their first lane of rax another big fight happening here with the bus actually winning the game at first started to go to the humans but eventually I bought managed to beat this stronger team as well we're still far away from beating pro teams but I think everyone here was surprised to see this I think the teamfight aspect of the bot was excellent like it it didn't mess up when it came to coordination it was some of the best like just a pure team fighting because it felt like I was just getting like hammered every single time that I made a mistake and I feel like nobody needs don't do that dota World Championships will take place in August while the best players in the world aren't getting ready to compete we're also working on the next version of our bot to see how far we've come will host a live match in July well we'll play a team of top players overall but we're excited about is that the training method we use is very general we're focused on learning dota but we're hoping that this will give us more and more insight about how a I can solve complex problems anytime that's my favorite part about when I hear the Bob makes advancement I don't get scared I get excited because I think this is another thing for me to have to challenge myself to be this is something that not a lot of people will be able to do let's have at it you [Music]

Original Description

We've created an AI system, OpenAI Five, which has started to defeat amateur human teams at Dota 2. This video contains an overview of our system, some example gameplay, and professional caster Blitz's analysis of our bot, as we start to gear up for playing a professional team at this year's Dota world championships, The International. See our blog post for more details: https://blog.openai.com/openai-five. Directed by: Jonas Schneider Starring: Christy Dennison Sound Supervisor: Larissa Schiavo Script Supervisor: Brooke Chan Production Manager: Diane Yoon Camera Operator: Frank Dellario, Manu Smith, Blake Tucker Stills Photographer: Eric Louis Haines, Brian Slaughter
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Playlist

Uploads from OpenAI · OpenAI · 9 of 60

1 Robots that Learn
Robots that Learn
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2 Emergence of Grounded Compositional Language in Multi-Agent Populations
Emergence of Grounded Compositional Language in Multi-Agent Populations
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3 OpenAI + Dota 2
OpenAI + Dota 2
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4 Dendi vs. OpenAI at The International 2017
Dendi vs. OpenAI at The International 2017
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5 Competitive Self-Play
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6 Learning a Hierarchy
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7 Physical Spam Detection
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8 Ingredients for Robotics Research
Ingredients for Robotics Research
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OpenAI Five
OpenAI Five
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10 OpenAI Five: Dota Gameplay
OpenAI Five: Dota Gameplay
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11 Learning Dexterity
Learning Dexterity
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12 Learning Dexterity: Uncut
Learning Dexterity: Uncut
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13 OpenAI Five Benchmark: Post-Game Analysis
OpenAI Five Benchmark: Post-Game Analysis
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14 Investigating Model Based RL for Continuous Control | Alex Botev | 2018 Summer Intern Open House
Investigating Model Based RL for Continuous Control | Alex Botev | 2018 Summer Intern Open House
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15 Generative Modelling | Sadhika Malladi | 2018 Summer Intern Open House
Generative Modelling | Sadhika Malladi | 2018 Summer Intern Open House
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16 A pathway to more efficient generative models | Will Grathwohl | 2018 Summer Intern Open House
A pathway to more efficient generative models | Will Grathwohl | 2018 Summer Intern Open House
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17 Learning Dexterity | Alex Ray | 2018 Summer Intern Open House
Learning Dexterity | Alex Ray | 2018 Summer Intern Open House
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18 Robust Vision-Based State Estimation | Hsiao-Yu 'Fish' Tung | 2018 Summer Intern Open House
Robust Vision-Based State Estimation | Hsiao-Yu 'Fish' Tung | 2018 Summer Intern Open House
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19 Using Semantic Trees In Place of Sentences | Munashe Shumba | OpenAI Scholars Demo Day 2018
Using Semantic Trees In Place of Sentences | Munashe Shumba | OpenAI Scholars Demo Day 2018
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20 Reinforcement Learning with Prediction-Based Rewards
Reinforcement Learning with Prediction-Based Rewards
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21 OpenAI Spinning Up in Deep RL Workshop
OpenAI Spinning Up in Deep RL Workshop
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22 Arena Announcement and Closing | OpenAI Five Finals (6/6)
Arena Announcement and Closing | OpenAI Five Finals (6/6)
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23 Co-Op Match | OpenAI Five Finals (5/6)
Co-Op Match | OpenAI Five Finals (5/6)
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24 OpenAI Five vs. OG, Game 2 | OpenAI Five Finals (4/6)
OpenAI Five vs. OG, Game 2 | OpenAI Five Finals (4/6)
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25 OpenAI Five vs. OG, Game 1 | OpenAI Five Finals (3/6)
OpenAI Five vs. OG, Game 1 | OpenAI Five Finals (3/6)
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26 Pre-Match Panel Discussion | OpenAI Five Finals (2/6)
Pre-Match Panel Discussion | OpenAI Five Finals (2/6)
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27 Opening Keynote | OpenAI Five Finals (1/6)
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28 OpenAI Robotics Symposium 2019
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29 OpenAI Scholars Demo Day 2019
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30 Multi-Agent Hide and Seek
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31 Solving Rubik’s Cube with a Robot Hand: Uncut
Solving Rubik’s Cube with a Robot Hand: Uncut
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32 Solving Rubik’s Cube with a Robot Hand: Perturbations
Solving Rubik’s Cube with a Robot Hand: Perturbations
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33 Solving Rubik’s Cube with a Robot Hand
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34 Music Generation | Christine Payne | OpenAI Scholars Demo Day 2018
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35 Deephypebot | Nadja Rhodes | OpenAI Scholars Demo Day 2018
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36 Physics Net | Ifu Aniemeka | OpenAI Scholars Demo Day 2018
Physics Net | Ifu Aniemeka | OpenAI Scholars Demo Day 2018
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37 Art Composition Attributes + CycleGAN | Holly Grimm | OpenAI Scholars Demo Day 2018
Art Composition Attributes + CycleGAN | Holly Grimm | OpenAI Scholars Demo Day 2018
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38 Generating Emotional Landscapes | Hannah Davis | OpenAI Scholars Demo Day 2018
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39 Looking For Grammar In All The Right Places | Alethea Power | OpenAI Scholars Demo Day 2020
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40 Semantic Parsing English to GraphQL | Andre Carerra | OpenAI Scholars Demo Day 2020
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41 Long term credit assignment with temporal reward transp… | Cathy Yeh | OpenAI Scholars Demo Day 2020
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42 Social learning in independent multi-agent reinfor… | Kamal N’dousse | OpenAI Scholars Demo Day 2020
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43 Quantifying Interpretability of Models Trained on Coi… | Jorge Orbay | OpenAI Scholars Demo Day 2020
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44 Towards Epileptic Seizure Prediction with Deep Network | Kata Slama | OpenAI Scholars Demo Day 2020
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45 Universal Adversarial Perturbations and Language M… | Pamela Mishkin | OpenAI Scholars Demo Day 2020
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46 Introductions by Sam Altman & Greg Brockman | OpenAI Scholars Demo Day 2020
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47 Introduction by Sam Altman | OpenAI Scholars Demo Day 2021
Introduction by Sam Altman | OpenAI Scholars Demo Day 2021
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48 Breaking Contrastive Models with the SET Card Game | Legg Yeung | OpenAI Scholars Demo Day 2021
Breaking Contrastive Models with the SET Card Game | Legg Yeung | OpenAI Scholars Demo Day 2021
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49 Large Scale Reward Modeling | Jonathan Ward | OpenAI Scholars Demo Day 2021
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50 Words to Bytes: Exploring Language Tokenizations | Sam Gbafa | OpenAI Scholars Demo Day 2021
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51 Learning Multiple Modes of Behavior in a Continuous… | Tyna Eloundou | OpenAI Scholars Demo Day 2021
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52 Scaling Laws for Language Transfer Learning | Christina Kim | OpenAI Scholars Demo Day 2021
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53 Contrastive Language Encoding | Ellie Kitanidis | OpenAI Scholars Demo Day 2021
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54 Characterizing Test Time Compute on Graph Structur… | Kudzo Ahegbebu | OpenAI Scholars Demo Day 2021
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55 Studying Scaling Laws for Transformer Architecture … | Shola Oyedele | OpenAI Scholars Demo Day 2021
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56 Feedback Loops in Opinion Modeling | Danielle Ensign | OpenAI Scholars Demo Day 2021
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57 Creating a Space Game with OpenAI Codex
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58 “Hello World” with OpenAI Codex
“Hello World” with OpenAI Codex
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59 Talking to Your Computer with OpenAI Codex
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60 Data Science with OpenAI Codex
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OpenAI Five, an AI system, uses reinforcement learning to defeat amateur human teams at Dota 2, demonstrating its ability to coordinate and focus as a team of five, with plans to challenge professional teams at the Dota World Championships. The system's training method is general and can be applied to other complex problems. The video provides an overview of the system, example gameplay, and analysis from professional caster Blitz.

Key Takeaways
  1. Train an AI system using reinforcement learning
  2. Apply the AI system to a complex game like Dota 2
  3. Analyze the gameplay and performance of the AI system
  4. Refine the AI system to improve its performance
  5. Challenge professional teams to test the AI system's limits
💡 The training method used by OpenAI Five is general and can be applied to other complex problems, making it a significant advancement in AI research.
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