Thanks, yes, that helpfully makes it more clear. To check if my understanding has improved, is this a better summary?
The agent is tasked with designing a second agent (aka policy), such that the second agent will achieve an expected return of X across many trials.
The second agent is a non-learning agent (aka frozen). It could be potentially expressed by a frozen neural net, or decision tree, or code. Because it is static, it could be analyzed by humans or other programs before being used.
I’m not sure. I guess it depends on what your definition of “agent” is. In my personal definition, following Yann LeCun’s recent whitepaper, the “agent” is a system with a number of different modules, one of it being a world model (in our case, an MDP that it can use to simulate consequences of possible policies), one of it being a policy (in our case, an ANN that takes states as inputs and gives action logits as outputs), and one module being a learning algorithm (in our case, a variant of Q-learning that uses the world model to learn a policy that achieves a certain goal).
The goal that the learning algorithm aims to find a suitable policy for is an aspiration-based goal: make the expected return equal some given value (or fall into some given interval).
As a consequence, when this agent behaves like this very often in various environments with various goals, we can expect it to meet its goals on average (under mild conditions on the sequence of environments and goals, such as sufficient probabilistic independence of stochastic parts of the environment and bounded returns, so that the law of large number applies).
Now regarding your suggestion that the learned policy (what you call the frozen net I think) could be checked by humans before being used: that is a good idea for environments and policies that are not too complex for humans to understand. In more complex cases, one might want to involve another AI that tries to prove the proposed policy is unsafe for reasons not taken into account in selecting it in the first place, and one can think of many variations in the spirit of “debate” or “constitutional AI” etc.
Thanks, yes, that helpfully makes it more clear. To check if my understanding has improved, is this a better summary?
The agent is tasked with designing a second agent (aka policy), such that the second agent will achieve an expected return of X across many trials.
The second agent is a non-learning agent (aka frozen). It could be potentially expressed by a frozen neural net, or decision tree, or code. Because it is static, it could be analyzed by humans or other programs before being used.
If so, then this sounds good to me. And is rather reminiscent of this other framing of such ideas: https://www.lesswrong.com/posts/sCJDstZrpCB8dQveA/using-uninterpretable-llms-to-generate-interpretable-ai-code
Hi Nathan,
I’m not sure. I guess it depends on what your definition of “agent” is. In my personal definition, following Yann LeCun’s recent whitepaper, the “agent” is a system with a number of different modules, one of it being a world model (in our case, an MDP that it can use to simulate consequences of possible policies), one of it being a policy (in our case, an ANN that takes states as inputs and gives action logits as outputs), and one module being a learning algorithm (in our case, a variant of Q-learning that uses the world model to learn a policy that achieves a certain goal). The goal that the learning algorithm aims to find a suitable policy for is an aspiration-based goal: make the expected return equal some given value (or fall into some given interval). As a consequence, when this agent behaves like this very often in various environments with various goals, we can expect it to meet its goals on average (under mild conditions on the sequence of environments and goals, such as sufficient probabilistic independence of stochastic parts of the environment and bounded returns, so that the law of large number applies).
Now regarding your suggestion that the learned policy (what you call the frozen net I think) could be checked by humans before being used: that is a good idea for environments and policies that are not too complex for humans to understand. In more complex cases, one might want to involve another AI that tries to prove the proposed policy is unsafe for reasons not taken into account in selecting it in the first place, and one can think of many variations in the spirit of “debate” or “constitutional AI” etc.
Thanks, that makes sense!