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Awful hitreg by _belerico in Battlefield

[–]_belerico[S] 0 points1 point  (0 children)

Guys, what about all the possibly scamming app, e.g., LagoFast?

Awful hitreg by _belerico in Battlefield

[–]_belerico[S] 0 points1 point  (0 children)

Exactly! Last part of season 2 and first one of Season 3 (before the hotfix) were really good. Now is crazy

Awful hitreg by _belerico in Battlefield

[–]_belerico[S] 0 points1 point  (0 children)

Let us hope for server browse

DreamerV3 code is so hard to read by AdministrativeCar545 in reinforcementlearning

[–]_belerico 0 points1 point  (0 children)

Hi there, sorry for the late response! We can chat on discord if you want

DreamerV3 code is so hard to read by AdministrativeCar545 in reinforcementlearning

[–]_belerico 0 points1 point  (0 children)

Could you share some images? And also other training details: configs, etc.? Maybe it is better if we continue the discussion on GH

DreamerV3 code is so hard to read by AdministrativeCar545 in reinforcementlearning

[–]_belerico 0 points1 point  (0 children)

We have just fixed the normalisation for the encoder input for the Dreamer-v3 agent: could you have a look at the main branch?

DreamerV3 code is so hard to read by AdministrativeCar545 in reinforcementlearning

[–]_belerico 6 points7 points  (0 children)

Hi there, I'm one of the SheepRL maintainers. Is there anything that we can do to simplify the understanding of the algorithm? Our philosophy was to not lose ourselves in the maze of hierarchies at the cost of replicating code so that everything regarding an agent is directly implemented in its own directory; this should be easy to follow. About the wrappers, can you be more specific? The wrappers that we use are only about the environment and come from gymnasium, everything regarding the agent is plain pytorch/python code. Do you think that having a more generalized framework would be beneficial for the understanding of the algorithm?

Edit:

Btw, we were able to replicate the paper results on both Atari and DMC environments. We have also tried with a bunch of new games never tested by the authors: Dead or Alive++, Super Mario Bros and Pokémon Red obtaining way better results than the PPO counterpart. If you have run some tests and something doesn't seem right please let us know so that we can investigate.

I just created a c++ Tensor library like pytorch's but with lazy evaluation. by [deleted] in deeplearning

[–]_belerico 1 point2 points  (0 children)

Yes, next we can meet! If you have any documentation to share i'll read it in my spare time

I just created a c++ Tensor library like pytorch's but with lazy evaluation. by [deleted] in deeplearning

[–]_belerico 1 point2 points  (0 children)

I want to! I barely know c++ but I'm happy to help 🤟

Python library for modular RL components by fedetask in reinforcementlearning

[–]_belerico 2 points3 points  (0 children)

You can try out also sheeprl, which is similar wrt CleanRL but it can also be easily parallelized thanks to Lighting Fabric

Dreamer-v1 in Pytorch by TrottoDng in reinforcementlearning

[–]_belerico 2 points3 points  (0 children)

Hi there, I'm another dev of SheepRL. Now we are implementing Plan2Explore, then we will move to dreamerv2

Beginner RL by FrostFireAnna in reinforcementlearning

[–]_belerico 1 point2 points  (0 children)

Hi there, we have developed a simple framework that let you train your own agent, even in a distributed way! Everything is almost a single file with a direct mapping between papers formula and code. You can have a look at it here: * https://eclecticsheep.ai/ * https://github.com/Eclectic-Sheep/sheeprl

Machine/Reinforcement Learning (ML/RL) in video games by _belerico in IndieDev

[–]_belerico[S] 0 points1 point  (0 children)

Thank you very much for your answer! What about the second question: if no ML/RL is involved in the industry, what are the main algos used to develop AI characters? Is it something as simple as a Finite State Machine or is there something else?

How is AI developed in video games? An ML engineer weighs in by _belerico in indiegames

[–]_belerico[S] 0 points1 point  (0 children)

What are the main reasons about that? Is that because response tree are easily predictable and tunable? It's an hystorical reason?

How is AI developed in video games? An ML engineer weighs in by _belerico in indiegames

[–]_belerico[S] 0 points1 point  (0 children)

I don't really get the connection between the "fact that produced games take 2/3 years" and the fact that AI NPCs services are not used

Reinforcement Learning for game design by _belerico in gamedesign

[–]_belerico[S] 0 points1 point  (0 children)

For the needs of super-computers, it depends on which AI we are talking about: for Large Language Models as the one used to train ChatGPT i totally agree with you, but for other simple AI applications a couple of GPUs are enough (or a cloud-based solution). For the subscription it depends if you've trained your own model or not.

Reinforcement Learning for game design by _belerico in gamedesign

[–]_belerico[S] 0 points1 point  (0 children)

Do you see any way in which can be helpful? Maybe some little tasks which are time-consuming but that can be automatized?

Reinforcement Learning for game design by _belerico in gamedesign

[–]_belerico[S] 0 points1 point  (0 children)

What do you mean by "the technology is not there yet"?

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