Federico Belotti

I'll re-open it so that we can track progress and use this issue to discuss about all the changes, i.e. * [ ] Models: the number of layers are fixed...

From @jmribeiro in #229: Hi @belerico Here it goes: > Which kind of observations do we want to support? The observations are custom made for an environment called "Level-Based Foraging"...

Hi @jmribeiro, sorry but we have focused our self-attention into fixing bugs and improving the overall usability of both the Dreamer-V3 agent and the library itself. We will have a...

Hi @jmribeiro, one solution that I thought is the following: * Every agent accepts the encoder (and the decoder when needed) from "outside", with the user selecting and customizing it...

@anthony0727 @michele-milesi thank you both! Effectively we save the `stochastic` and `recurrent` state that refers to the previous action instead of the one that has been generated

Hi @defrag-bambino, the slowdown when raising the replay-ratio is expected, as the higher the replay-ratio the more gradient steps are computed by the agent per policy-step. Since the training steps...

Hi @defrag-bambino, have [this](https://github.com/Eclectic-Sheep/sheeprl/issues/261#issuecomment-2185221670) fixed your issue? Are there any other consideration that you want to share?

Hi @redzhepdx! Thank you for the suggestions, really appreciated them! We can definitely have something similar to [this](https://github.com/araffin/rl-tutorial-jnrr19/blob/sb3/1_getting_started.ipynb) and [this](https://docs.omniverse.nvidia.com/isaacsim/latest/isaac_gym_tutorials/tutorial_advanced_rl_stable_baselines.html): what do you think @michele-milesi? For the contribution we have...

Hi @geranim0, if this is done we can add this feature in a new PR and put it in the next release

Hi @drblallo, thank you for your words! You're right, self-play is not supported right now. Do you have any specific references about self-play that we can look upon?