Hey everyone! I’ve been diving into the PPO algorithm in a popular RL toolkit. I’m a bit lost about two key parameters: optim_batchsize and timesteps_per_actorbatch. What do these do exactly?
Also, I noticed the framework uses some special wrappers for Atari games. One of them ends episodes when a life is lost. This seems to make episodes super short at first (like 7-8 steps). But the batch size is way bigger (256). How does this work? Wouldn’t it mess up the updates?
I’m trying to get my head around how all these pieces fit together. Any insights would be awesome! Thanks!
yo TomDream42, those params can be confusing. optim_batchsize is how many samples per optimization step, while timesteps_per_actorbatch is steps before updating. for atari, don’t stress about short episodes. ppo handles variable lengths fine. the bigger batch size helps get enough diverse data for good updates. keep experimenting and you’ll figure it out!
hey TomDream42, those params are tricky. optim_batchsize means how many samples per opt step while timesteps_per_actorbatch is the number of steps before update. as for atari, ppo handles short episodes by working with variable step counts - no worries!
I’ve worked extensively with PPO, and those parameters can definitely be confusing at first. From my experience, optim_batchsize controls how many samples are used in each optimization step, which affects the stability of learning. timesteps_per_actorbatch determines how many environment steps are collected before performing an update, impacting the trade-off between sample efficiency and update frequency.
Regarding the Atari wrappers, ending episodes on life loss is a common technique to provide denser rewards. In practice, I’ve found it doesn’t negatively impact learning as much as you might expect. PPO is designed to handle variable-length episodes, so it can effectively utilize the information from these shorter segments. The larger batch size helps ensure enough diverse experiences are collected for stable updates, even with shorter individual episodes.
It took me a while to grasp how all these components interact, but experimenting with different settings really helped solidify my understanding. Keep at it, and you’ll get there!
Having worked with PPO implementations, I can shed some light on those parameters. optim_batchsize controls the number of samples used in each gradient step during optimization. It’s crucial for balancing learning stability and computational efficiency. timesteps_per_actorbatch determines how many environment steps are collected before performing a policy update, affecting the trade-off between data freshness and update frequency.
Regarding the Atari wrappers, ending episodes on life loss is a common technique to provide more frequent feedback to the agent. While it does result in shorter initial episodes, PPO is designed to handle variable-length trajectories effectively. The larger batch size ensures that enough diverse experiences are collected across multiple short episodes to perform meaningful updates.
In practice, these design choices often lead to more stable and efficient learning, especially in environments with sparse rewards like many Atari games.