The maker of ChatGPT had made progress on Q* (pronounced Q-Star), which some internally believe could be a breakthrough in the startup’s search for superintelligence, also known as artificial general intelligence (AGI), one of the people told Reuters. OpenAI defines AGI as AI systems that are smarter than humans. Given vast computing resources, the new model was able to solve certain mathematical problems, the person said on condition of anonymity because they were not authorized to speak on behalf of the company. Though only performing math on the level of grade-school students, acing such tests made researchers very optimistic about Q*’s future success, the source said.
Q → Q-learning: Q-learning is a model-free reinforcement learning algorithm that learns an action-value function (called the Q-function) to estimate the long-term reward of taking a given action in a particular state.
* → AlphaSTAR: DeepMind trained AlphaStar years ago, which was an AI agent that defeated professional StarCraft players.
They also used a multi-agent setup where they trained both a Protoss agent and Zerg agent separately to master those factions rather than try to master all at once.
For their RL algorithm, DeepMind used a specialized variant of PPO/D4PG adapted for complex multi-agent scenarios like StarCraft.
Now, I’m hearing that there’s another model too: Zero.
Well, if that’s the case:
1) Q* → Q-learning + AlphaStar
2) Zero → AlphaZero + ??
The key difference between AlphaStar and AlphaZero is that AlphaZero uses MCTS while AlphaStar primarily relies on neural networks to understand and interact with the complex environment.
MCTS is expensive to run.
The Monte Carlo tree search (MCTS) algorithm looks ahead at possible futures and evaluates the best move to make. This made AlphaZero’s gameplay more precise.
So:
Q-learning is strong in learning optimal actions through trial and error, adapting to environments where a predictive model is not available or is too complex.
MCTS, on the other hand, excels in planning and decision-making by simulating possible futures. By integrating these methods, an AI system can learn from its environment while also being able to anticipate and strategize about future states.
One of the holy grails of AGI is the ability of a system to adapt to a wide range of environments and generalize from one situation to another. The adaptive nature of Q-learning combined with the predictive and strategic capabilities of MCTS could push an AI system closer to this goal. It could allow an AI to not only learn effectively from its environment but also to anticipate future scenarios and adapt its strategies accordingly.
Conclusion: I have no idea if this is what the Q* or Zero codenames are pointing to, but if we play along, it could be that Zero is using some form of Q-learning in addition to Monte-Carlo tree search to help with decision-making and Q* is doing a similar thing, but without MCTS. Or, I could be way off-track.
Regarding Q*, the (and Zero, the other OpenAI AI model you didn’t know about)
Let’s play word association with Q*:
From Reuters article:
Q → Q-learning: Q-learning is a model-free reinforcement learning algorithm that learns an action-value function (called the Q-function) to estimate the long-term reward of taking a given action in a particular state.
*→ AlphaSTAR: DeepMind trained AlphaStar years ago, which was an AI agent that defeated professional StarCraft players.They also used a multi-agent setup where they trained both a Protoss agent and Zerg agent separately to master those factions rather than try to master all at once.
For their RL algorithm, DeepMind used a specialized variant of PPO/D4PG adapted for complex multi-agent scenarios like StarCraft.
Now, I’m hearing that there’s another model too: Zero.
Well, if that’s the case:
1) Q* → Q-learning + AlphaStar
2) Zero → AlphaZero + ??
The key difference between AlphaStar and AlphaZero is that AlphaZero uses MCTS while AlphaStar primarily relies on neural networks to understand and interact with the complex environment.
MCTS is expensive to run.
The Monte Carlo tree search (MCTS) algorithm looks ahead at possible futures and evaluates the best move to make. This made AlphaZero’s gameplay more precise.
So:
Q-learning is strong in learning optimal actions through trial and error, adapting to environments where a predictive model is not available or is too complex.
MCTS, on the other hand, excels in planning and decision-making by simulating possible futures. By integrating these methods, an AI system can learn from its environment while also being able to anticipate and strategize about future states.
One of the holy grails of AGI is the ability of a system to adapt to a wide range of environments and generalize from one situation to another. The adaptive nature of Q-learning combined with the predictive and strategic capabilities of MCTS could push an AI system closer to this goal. It could allow an AI to not only learn effectively from its environment but also to anticipate future scenarios and adapt its strategies accordingly.
Conclusion: I have no idea if this is what the Q* or Zero codenames are pointing to, but if we play along, it could be that Zero is using some form of Q-learning in addition to Monte-Carlo tree search to help with decision-making and Q* is doing a similar thing, but without MCTS. Or, I could be way off-track.