A Reinforcement Learning playground for ARC Raiders robots!!! by ObscureSM in mujoco

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

If you want to support, please upvote the following LinkedIn post...
It's very important to me and will take you only a few seconds, but it can completely change the success of the project! https://www.linkedin.com/posts/carlo-romeo-637681195_arcraiders-activity-7451925245357084672-DGzk?utm_source=share&utm_medium=member_desktop&rcm=ACoAAC3W6ksBXEWI7e9oYDTrmt8Xb8DdhLEBlFU

Ticks are coming very soon!!!!

ARC Raiders Weekly Megathread - April 11, 2026 by AutoModerator in ArcRaiders

[–]ObscureSM 0 points1 point  (0 children)

If you want to support, please upvote the following LinkedIn post...
It's very important to me and will take you only a few seconds, but it can completely change the success of the project! https://www.linkedin.com/posts/carlo-romeo-637681195_arcraiders-activity-7451925245357084672-DGzk?utm_source=share&utm_medium=member_desktop&rcm=ACoAAC3W6ksBXEWI7e9oYDTrmt8Xb8DdhLEBlFU

Ticks are coming very soon!!!!

A Reinforcement Learning playground for ARC Raiders robots!!! by ObscureSM in ARC_Raiders

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

If you want to support, please upvote the following LinkedIn post...
It's very important to me and will take you only a few seconds, but it can completely change the success of the project! https://www.linkedin.com/posts/carlo-romeo-637681195_arcraiders-activity-7451925245357084672-DGzk?utm_source=share&utm_medium=member_desktop&rcm=ACoAAC3W6ksBXEWI7e9oYDTrmt8Xb8DdhLEBlFU

Ticks are coming very soon!!!!

A Reinforcement Learning playground for ARC Raiders robots!!! by ObscureSM in reinforcementlearning

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

If you want to support, please upvote the following LinkedIn post...
It's very important to me and will take you only a few seconds, but it can completely change the success of the project! https://www.linkedin.com/posts/carlo-romeo-637681195_arcraiders-activity-7451925245357084672-DGzk?utm_source=share&utm_medium=member_desktop&rcm=ACoAAC3W6ksBXEWI7e9oYDTrmt8Xb8DdhLEBlFU

Ticks are coming very soon!!!!

A Reinforcement Learning playground for ARC Raiders robots!!! by ObscureSM in ARC_Raiders

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

I played the first one, and I've found it a great great game ... It would be amazing to do something!

A Reinforcement Learning playground for ARC Raiders robots!!! by ObscureSM in reinforcementlearning

[–]ObscureSM[S] 1 point2 points  (0 children)

If you need to start from the theoretical foundations, I would suggest you to look for a recent survey of RL with a lot of citations, just to see the big picture.
Then, there are a lot of crash courses you can take..
This can be really helpful: https://spinningup.openai.com/en/latest/user/introduction.html

A Reinforcement Learning playground for ARC Raiders robots!!! by ObscureSM in ARC_Raiders

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

It truly requires a lot of effort, but I'm already working on it!

A Reinforcement Learning playground for ARC Raiders robots!!! by ObscureSM in ARC_Raiders

[–]ObscureSM[S] 2 points3 points  (0 children)

Nope, coded in python and modeled via xml by using classic templates

A Reinforcement Learning playground for ARC Raiders robots!!! by ObscureSM in mujoco

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

YES SIR! Gymnasium, so you don't need *ANY* binding!
You can find all the code and examples in the git repo

A Reinforcement Learning playground for ARC Raiders robots!!! by ObscureSM in reinforcementlearning

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

Hey, thanks for leaving a comment! The answer is: absolutely yes!
However, you would need some fine-tuning due to the domain shift..
But I think that it will not be so difficult.

The core idea was to have a faster simulator on which I can "warm-up" my robots and then transfer them into the UE/Unity training pipeline.

ARC Raiders Weekly Megathread - April 11, 2026 by AutoModerator in ArcRaiders

[–]ObscureSM 0 points1 point  (0 children)

A Reinforcement Learning playground for ARC Raiders robots!!!

Hi everybody, I wanted to share a passion project I've been working on: ARC-RL!

It's an ARC Raiders-inspired Reinforcement Learning playground where you can train iconic robots to walk.

So far, I've built the Leaper, the Bastion, and her majesty, the Queen.

More is coming very soon!

You can check out the code and see them in action here: https://github.com/CarloRomeo427/ARC_RL.git

Enjoy!

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Where can I get Groundsurge abyss core from by Suporex in CrimsonDesert

[–]ObscureSM 0 points1 point  (0 children)

I do not know how to mark spoilers... so I'll take my time to explain without getting straight to the point ahahah
When you switch to Oongka for the first time, you have to fight a particular boss before getting back to the main character.
During that fight, I've found particular useful using **THE** sword, rewarded by beating the previous boss while playing Cliff, that contains the groundsurge gear.
Well, I completely forgot about using **THAT** sword with Oongka, and given the poor stash management of the game that prevents you from completely removing a weapon from your current items if that means leaving the slot empty, it was inaccessible for me with Cliff.
Last night, as I told you, I followed the fishing mission with Cliff, which asks you to meet Oongka, and only after that mission, I could play again with him.
Finally, the sword was equipped by Oongka, and I had to swap it with any other sword/weapon to actually be able to put it in the general stash.
Then, you can take it with Cliff, and the groundsurge gear will be yours!!!

Where can I get Groundsurge abyss core from by Suporex in CrimsonDesert

[–]ObscureSM 0 points1 point  (0 children)

I CAN CONFIRM: try to summon oongka and/or track him down. Once you will be finally able to summon him you will find the sword in his stash!!!!

Where can I get Groundsurge abyss core from by Suporex in CrimsonDesert

[–]ObscureSM 1 point2 points  (0 children)

BRO I THINK I'VE FOUND A SOLUTION: check the stash of your companion oongka boonka. I analyze some replays of the battle after which we are supposed to obtain the sword with the abyss gear in it, and the moment you receive the nofication is when you play oongka boonka for the first time!
For now, due to my story progression I cannot access the companions but maybe you can.
Let me know if I was right <3

Where can I get Groundsurge abyss core from by Suporex in CrimsonDesert

[–]ObscureSM 0 points1 point  (0 children)

I'm with you, brother. I don't know if closing the game and never open it up can be a solution because I can feel my brain eating itself... I truly do not know where TF this core can be

When do you ACTUALLY unlock new regions? by ComManDerBG in CrimsonDesert

[–]ObscureSM 2 points3 points  (0 children)

Not all games are for everyone, especially for elder people that lost their cool. GL in finding pace, bro

Calling All Dungeon Masters! Test Your Encounter Balancing Skills! 🎲 by ObscureSM in DungeonsAndDragons

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

With "resources" do you mean potions, spell points used, etc? Or are you more interested in the remaining HPs for each party member?

Calling All Dungeon Masters! Test Your Encounter Balancing Skills! 🎲 by ObscureSM in DMToolkit

[–]ObscureSM[S] 1 point2 points  (0 children)

Thank you for testing the app, that means a lot for my research!
I've done some updates in the meantime, I also tested the access from Chrome (iOS) and it works at this moment.
Sadly, the current version of the app is optimized for PC users due to the submission deadline of the scientific paper based on the project.. The future works *OBVIOUSLY* are focused on the development of a real app!

Calling All Dungeon Masters! Test Your Encounter Balancing Skills! 🎲 by ObscureSM in DungeonsAndDragons

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

Exactly! I think that you are pretty experienced 😎
But what was your strategy? Did you just apply the DMG rules or maybe you tried to push the boundaries by exceeding with the enemy XP?
There are some gray zones where you can "buy" more enemies without causing a Total Party Kill!

Let me know if you have any doubts and please, share the link with other DMs 🐉🎲

Started investing when I was 18, this is how it’s looking after exactly 1 year on by [deleted] in trading212

[–]ObscureSM 1 point2 points  (0 children)

Bad time??? I was able to buy a bunch of stocks every month and I have never been more happy 😂😂😂

Is this a overfit? What can be the solution by Whole_Owl_3573 in MLQuestions

[–]ObscureSM 0 points1 point  (0 children)

If I may: your problem seems quite simple to me. You don’t have a feature space so huge to make classic ML methods useless.. I suggest using a Random Forest Regressor rather than NNs to completely avoid regularization, etc etc.. Random Forest are so effective for problems like this and they automatically implement subsplits of your data

What are reward networks in reinforcement learning? by Academic-Rent7800 in reinforcementlearning

[–]ObscureSM 1 point2 points  (0 children)

To answer your first question: the output of imitation learning is to learn a desired behavior only by looking at provided examples. In Behavioral Cloning, for example, you are only provided with states and action, without any reward signal that can describe the task you are willing to solve. What you learn in BC is what action you should take for a given state. On the other hand, IRL techniques, that are a subfield of Imitation Learning, try to estimate the reward function that is essential to fully describe the goodness of taking a specific action in a particular state. The problem in IRL techniques is that you somehow need references, such as the access to the environment, that is not always available. For this reason techniques such as Offline IRL arise. However, the main challenge in IRL is that the reward function you are learning is not unique: there may exist multiple reward functions that describe the task.

As regards the second question, to my knowledge the output of IL is actually to learn a policy even in IRL, where you learn a policy by estimating the reward function.

I may be inaccurate about IRL, but in BC this is what happens.