@philtabor,
This is an intriguing implementation of PPO2. It is simple and it converges for cartpole quicker than any other I have seen. Taking a basic definition of "convergence" as 10 episodes in a row at total reward=max reward (200), this converges in ~230 episodes.
I tested a version with all pytorch functions converted to identical tensorflow 2.3 functions, and adding two gradient tapes to the .learn() function. It doesn't converge nearly as well. Do you have any idea why? is it a characteristic of pytorch that makes this implementation so successful?
Apologies if I have posted this twice, I am new to github.
@philtabor, This is an intriguing implementation of PPO2. It is simple and it converges for cartpole quicker than any other I have seen. Taking a basic definition of "convergence" as 10 episodes in a row at total reward=max reward (200), this converges in ~230 episodes.
I tested a version with all pytorch functions converted to identical tensorflow 2.3 functions, and adding two gradient tapes to the .learn() function. It doesn't converge nearly as well. Do you have any idea why? is it a characteristic of pytorch that makes this implementation so successful?
Apologies if I have posted this twice, I am new to github.