What does this refer to: cartpoll demo famously tripped up derivative based reinforcement learning
The phrase "cartpole demo famously tripped up derivative-based reinforcement learning" is likely referring to a classic problem in the field of reinforcement learning, which involves balancing a pole on a cart. The pole is attached to the cart via a hinge, and the goal is to keep the pole upright by moving the cart left or right in response to its angle. This problem is often used as a benchmark for testing reinforcement learning algorithms.
The phrase suggests that derivative-based reinforcement learning algorithms, which rely on computing gradients of a function with respect to its parameters, were not successful at solving this problem. This could be due to the fact that the problem is highly non-linear and requires precise control, which may be difficult to achieve with gradient-based methods.
Edit: bard got it too, with more detail, which is surprising
You should be really careful with asking this kind of question to ChatGPT, because now you think you've learned the answer, but in fact there are two options with very different outcome:
- ChatGPT was trained on a corpus of data containing the answer and is able to give you a decent answer
- ChatGPT was never exposed to the answer and will hallucinate a plausible-sounding response, and because it will answer in a really convincing way, you'll get tricked into believing complete bullshit
What does this refer to: cartpoll demo famously tripped up derivative based reinforcement learning
The phrase "cartpole demo famously tripped up derivative-based reinforcement learning" is likely referring to a classic problem in the field of reinforcement learning, which involves balancing a pole on a cart. The pole is attached to the cart via a hinge, and the goal is to keep the pole upright by moving the cart left or right in response to its angle. This problem is often used as a benchmark for testing reinforcement learning algorithms.
The phrase suggests that derivative-based reinforcement learning algorithms, which rely on computing gradients of a function with respect to its parameters, were not successful at solving this problem. This could be due to the fact that the problem is highly non-linear and requires precise control, which may be difficult to achieve with gradient-based methods.
Edit: bard got it too, with more detail, which is surprising