Leaderboard

Up to now 0ad doesn't offer the ability for users to change their passwords for the multiplayer lobby. However, that'd be a great feature to have and has in fact been already a feature request for several years (https://trac.wildfiregames.com/ticket/2543). I had a quick look and believe this should be pretty straight forward to implement, as the server-side as well as the XMPP library used by 0ad already support that. So all left to implement it is to add some glue code to 0ad and build the UI for it. If you're interested in helping out and implementing this feature or have any questions, please reply to this post or send me a PM.

Horseriders don't need to look where they're walking and can focus on the environment? (Intentionally not stating any preference in this discussion.)

Hello there! In anticipation of my (also Mediterranean) vacation in Italy I want to publish the last map I mainly built in November to December 2021. As new Aegean textures came out in Alpha 25 the summer a year ago I tried to make some map out of them (I can remember @wowgetoffyourcellphone of wishing my Map "Metropolis" having used the new terrains ). After my maps "The Legend of Avilava" and "Metropolis" (both scenarios) I decided to resort to the Skirmish type of map giving the player more individuality by choosing their own civ. Thus, there are no starting structures / little cities besides the original headquaters each player beginns with. Maybe I'm too generous in the assumption everyone playing 0ad has a proper gaming PC. I just like these huge maps (even beyond "giant" would be great) with gigantic battles and stuff, hence I've decided it's scale being "giant". The map generally is arranged to have a 4 vs 4 setting. Each player starts near his own team; far from enemy players. Relatively the same amount of starting resources near the headquaters accompany each player; every team should have land more or less equally distributed to gain resources from, to build and / or to expand on. Having let some simulations with bots go their way, I've had a lot of fun watching them. Through this I've been able to correct some little flaws and some features that would hinder the game's fluency. It's likely this map isn't perfect or "fine" anyway (others have to say so), thus I imagine that I'll maybe make some further versions. And if others like to do so, you're granted permission to of course. You guys feel free to use this map in which way you want, it's yours! But I think now it's time to show you some screenshots to get a better understanding of what I'm talking about. Best to show you the type of Mediterranean landscape that I love so much (regarding this I sadly live in Austria :D) and which I hope I've been able to properly reconstruct here: Also a look onto the starting positions of each player (screenshot follows player number): (Team 1: Player 1 - Player 4; Team 2: Player 5 - Player 8) Looking back, I'm happy enough with what came out of my conception of a Mediterranean "holyday-like" map. It's too good I'm now starting with feeling it all myself in my vacation in Italy! As for you guys, as I said - The map is all yours. Thank the 0ad programmers for having made this aweseome game and featuring the forum to have the gamers able to communicate and exchange with each other - and to publish self made maps! Cya soon, this Saturday I'm out in Italy now yooooo! Aegean Archipelago.xmlAegean Archipelago.pmp Edit on 13.8.2022: The edited versions should be preferred I hope there are no problems with downloading them? Aegean Archipelago First Edit.pmpAegean Archipelago First Edit.xml (When a number in front of the map file's name occur, you have to delete this part of the file name. For example, after downloading, you may get the files with a certain number, like: "678349583_AegeanArchipelagoFirstEdit.pmp" and "678349583_AegeanArchipelagoFirstEdit.xml" - you have to delete "678349583_" of the file names to be able to play on the map)

If anyone is wondering what happens during a game of 0ad, here is the answer: (Any browser should be able to open the attached svg file) 0ad_pprof22027.0.svg

Hey everyone, I am creating this thread to discuss the balancing from a "meta" perspective in hopes to improve the current situation. Please refrain from discussing "actual" balancing in this thread. As some of you may know, it has been a priority of mine for the past three years to find a way to provide a good experience for players, whether they are on the competitive side or not. For the 24th Alpha I created a Personal Mail (PM) with a few individuals, in order to try to create a team-like cohesion. While the 24th Alpha was a game changer in a lot of ways, and a painful experience, I do believe that it was the most productive in terms of actual patches and changes. People played by the rules by proposing, creating, and accepting patches (I insist on the last part because without formal acceptance there is no liability). I also tried to leverage a documentation team to update the design document. The job was too hard, and it died quickly and in silence. The original design was somewhat different from what the current game is today. There would have been much less civilizations, and instead your empire would have switched from a generic civilization to a more specific one (e.g greek -> macedon -> seleucid). Then another era came, where everything changed. Meanwhile, I was not happy about doing things in the shadows. This whole project is a community effort, and the contributors of today are the ones that might carry the flame when I am gone. So after a few internal discussions we came up with the balancing subforum, where everyone could see what was going on behind the curtains but only a selected few could interract, and anyone could ask me for a seat at the table, with some relevant experience. It came with little titles that hoped would boost morale. However... It did not go as I(we) planned. It created an even bigger split in the community, resulted in a huge variety of threads which is good, but which led nowhere as none of the idea was accepted enough to be implemented. And even the ideas who did get implemented did not make it. While the Personal Mail (PM) did go off track from time to times, it had the advantage of notifying people everytime someone posted (I do not think many people use that forum feature for threads) and I think the discussion was more focused, people pinging each other for patch reviews etc... There was also an attempt to use the chat on Phabricator which also sends emails for each messages, but it quickly vanished after the 24th Alpha. The 25th Alpha got some welcomed changes, at a much slower rate, and I think it fixed a lot of the quirks Alpha 24 introduced. But we're not quite there yet, and I know a few people are hurt about the current state of the game. I'm not a fan of the current balancing forums, and I'd like to merge them again with the rest of the game discussions, or at least to open them, since there seem to be no point in having them closed anymore. So I'm asking everyone, what can we (as in Wildfire Games) do, to get more contributions about balancing, to make A26 a success. We have some very nasty release blockers, and that leaves time for a bit more balancing patches. Best regards, Stan

If you're familiar with git you can put it on Github / Gitlab. If not you can consider learning it

I believe that won't be necessary, as sharing accounts isn't permitted anyway and loosing access to a shared account does already happen right now when the account gets banned for any reason.

He means that https://code.wildfiregames.com/D1730 Where the game doesn't actually start till you click okay so you can scroll tips.

We currently don't collect enough information to implement a "forgot password" feature. This is for changing passwords after the account has already been authed. Which could be implemented via another tab to the prelobby page or something inside the lobby interface, I am not sure.

use a different map.

Yes, you just need to change the map type. We have 'scenarios' which have the number of players and their civilizations locked, 'skirmishes' which only have the number of players locked an lastly 'random' maps, where you can choose the amount of players. So in the menu to start a game you need to click on the map imagine and then choose one of the random maps. Then you can change the number of players.

But the vision is still longer if both units aren’t moving. The logic is inconsistent. A bit off topic, but this reminds me of how I think vision range should reflect what a unit is doing at any given time so that units actively engaged in eco/fighting should have the shortest vision, units walking/riding should have medium ranges vision, and units standing still should have the longest vision. That seems far more realistic than the current scenario and gets at the “distraction” idea you bring up

Community Update (8/02/22) Over the course of a few months, we have continued to update our community centered-operations. Today we wish to share those changes and state our plans for the future. Discord Server Our Discord Server is used for many things. It is a community center at heart but also is a hub for everything else. Regular updates, exclusive polls, and an archive come together to make our Discord Server the thriving heart of our community. Our archive includes: Art, Music, Mod Lore, Cutscenes, and more. We are always active and ready to answer any questions you may have and take suggestions on lore, game mechanics, or other things you would like to see in the mod. We always love hearing from you. Wiki (Fandom) We have just begun to break ground on the Fandom platform in order to create a readily available repository of information regarding the mod's lore, mechanics, and more. The Wiki page is unfinished but it has an almost defined framework and we thought it was worth reporting on. Sub-Reddit A small but valuable piece of our community, our Sub-Reddit is basically our forum dedicated wholly to community interaction. We do post content reports here and there but the real goal is to respond to our community and create a friendly environment for discussion. Future Plans: Our current priority is to add our first faction to the game, The Adrestian Empire. The Empire will be the first to join the mod because of the result of a community poll we had a while ago. You can find more information on the Adrestian Empire here in one of our articles. We are also planning to expand our Wiki and continue to add content to it going beyond what it is at the moment. If you wish to help in any way, just let us know on our Discord Server or Sub-Reddit, and thank you. We are trying to avoid spoilers for the mod entirely in this update but we can give you this hint: the "Three Wars" in the title is not just for sake of naming it. Thank you for reading our Community Content Report for August 2, 2022, we hope you have a wonderful day.

After some time I again managed to play around with the code and finish some other boats and heroes. 1. Biome variants: some trees typical for winter environment provide an "aura", which tells units to use specific visual variants - who would walk in sandals on snow? 2. Another minor update concerns monoxyla: melee units help with rowing, making the boat faster, while ranged units slow it down, but they can fire from the board. 3. Hunters (basic archers) can now swim; while swimming, ranged units can use only melee attacks. 4. Ponies! Cavalry units use smaller horses - only elite riders switch to high ones. I guess this is more historical for the setting. Not that it matters much, but maybe you would like to use the animations.

Are the imposters good players? Is this finally my chance to "defeat" a 1600 rated player?

If anything, we have plenty of evidence of bribery and treachery among the class of people from which we pull the game's "heroes."

Amazing! I really wish we had more natural looking maps like these, the shorelines, hills, ridges and scenic combat that can take place in these places. Excellent work!

@Stan`suggested I just add a custom template, which I did, and now the Fert maps should work as intended. https://github.com/0ad-matters/community-maps-2/commit/d9ca9878d42509f0edf4db0c7c1b301295739ebb

This is possibly nothing new - boats can be now built with turret mechanics. Units transported by them will sit down unless they have something to attack in range. Currently, the boat also slows down proportionally to the number of passengers.

Indeed interesting.

Another update brings a new unit and a new feature. The unit - Child - acts as a cheaper "civilian" unit, which is less effective at hard works like lumbering and mining, but gathers fruit well. Boys have a bonus to carrying capacity, girls have a higher resistance. As my grandfather always reminded me that Romans found it important to know how to read, write and swim, I always found it somewhat surprising, that in most Rome-themed games not even Velites can swim. This is, of course, not acceptable for a light-infantry-focused faction. Thus, Warriors - both basic and skull-upgraded - are now able to swim. That is, they can traverse deep water with a small penalty to moving speed. A hack was needed in UnitAI component to make the specific animations for idle, moving and attack states. "don't mind us, we're just having a bath" Little update - now I understand how to calculate the depth, so the swimming mode should be triggered correctly.

Hello everyone, I have been interested in making it possible to explore applications of machine learning in 0 AD (as some of you may have gathered from https://trac.wildfiregames.com/ticket/5548 ). I realized that I haven't really explained very thoroughly my interest and motivation so I figured I would do so here and see what everyone thinks! tl;dr - At a high level, I think that adding an OpenAI gym-like interface* could be a cool addition to 0 AD that would benefit both 0 AD (technically and in terms of publicity) as well as the research community in machine learning and AI. I go into the specifics below as well as discuss other potential avenues for integrating/leveraging machine learning: Potential Machine Learning Problems/Applications Intelligent unit control (micromanagement) I have an example where an AI learns to kite with cavalry archers when fighting infantry at https://github.com/brollb/simple-0ad-example. This is probably one of the easiest problems to explore as it can be done progressively starting with small, clearly defined scenarios using the functionality added in the beforementioned ticket. That said, there are still some of the standard challenges present with machine learning around ensuring that the AI has been trained on sufficiently diverse scenarios so that it doesn't ever encounter something new and behave incorrectly. As far as potential impact on the game, automatic micromanagement could be interesting for either a component in an otherwise scripted AI such as Petra or as a way to make the units more intelligent as they gain experience. That is, I could imagine that as the units gain more experience, they could also start having improved tactical behavior, such as kiting, automatically. Enemy AI Trained Entirely with Reinforcement Learning This is actually very difficult although it has been recently done in StarCraft 2 (https://deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii). Although I think this could be fun for people to try to do, I wouldn't have high expectations on this front for awhile because it is a very hard problem for ML to solve - especially given the large number of different civilizations, maps, resource types, etc. Enemy AI with Scripting and Learned Components This is referring to a generic version of what I mentioned under "intelligent unit control". Essentially, there are a lot of opportunities to incorporate learned components into an otherwise scripted AI. From a technical perspective, this makes the machine learning problem much easier/tractable while still enabling more intelligent behavior from the built in AI. There are many different examples of intelligent components that could be incorporated. For example, it could try to predict the outcome of a battle (to determine if we should retreat) or try to imitate various high-level human strategies (such as predicting what a human might target for an attack). Quantitative Game Balancing This is a very interesting problem and I find 0 AD to be a particularly unique opportunity for exploring it. Essentially, the idea is that there are many different parameters in a game (such as attack damage for each unit, etc) which are quite difficult to tune without making the game imbalanced and one of the civilizations/strategies OP. (I don't think I need an example for this community but I enjoyed watching https://www.gdcvault.com/play/1012211/Design-in-Detail-Changing-the.) This problem is nontrivial since detecting overpowered strategies really requires an understanding of the way various aspects of the game can be exploited. Although this is a nontrivial problem, I find it to be an exciting opportunity for 0 AD to gain publicity and for researchers to have a sandbox in which they can explore this research question in an actual game (rather than a trivial, toy environment). That is, many of the other environments used in reinforcement learning research are either open source toy environments (eg, CartPole) or proprietary games which cannot be modified (eg, StarCraft 2). There has been a bit of related research in detecting imbalance in complex games like StarCraft 2 as well as balancing simpler games but as proprietary games will not be exposing the parameters used for the units (and other aspects of the game), automatic game balancing approaches are limited. Being an open source game that people actually play, 0 AD provides a really exciting opportunity for research in this direction as the parameters of the game are not proprietary and could be modified programmatically enabling researchers to explore this rather complex problem. For the 0 AD community, enabling researchers to conduct this type of research in the game itself should make it much easier to be able to incorporate any results of such research into the game making 0 AD more fun and an even better game! Imitation Learning Training the AI to imitate humans is worth mentioning although the impact on the game is likely to be in one of the beforementioned ways. Imitation learning, unlike reinforcement learning, is training the AI using expert demonstrations of gameplay. It is often used as a method for essentially initializing the AI to something reasonable before training it further with reinforcement learning (ie, training the AI using a reward rather than example). Imitation learning can arguably be more valuable for game development given that it can more directly instill various human-like behaviors (hopefully making the gameplay more engaging and interesting) rather than simply trying to maximize some reward or score in the game. Techniques to Train and Understand AI Agents This is more of a general research direction that I find interesting (and is similar to research that I have done in the past). Essentially, this is exploring the means by which the game developer can use the various methods of instilling behavior into an AI (programming, reinforcement learning, imitation learning) to create the desired behavior (and game experience). This is a bit of both a human-computer interaction (HCI) and machine learning question (also related to machine teaching). To give a more concrete example, this would include exploring the behavior of a trained RL agent in the game, correcting these behaviors, and perhaps trying to detect potentially incorrect behaviors to raise to the user automatically. 0 AD is well suited for this type of research for the same reasons that it is well suited for exploring game balance - most games used in research are either proprietary or not something people would actually play. Optimizing Existing Game Parameters (Relatively Easy) There are some existing machine learning tricks that could be used to make other sorts of improvements to the game rather than explore research questions. A while back, I was playing around with CMAES (a machine learning technique to optimize a set of parameters given a "fitness function") to improve some of the sort of magic numbers used within Petra such as "popPhase2" and "armyMergeSize". Essentially, this made it possible to find values for these parameters which would improve the AI's ability to win when playing against the standard Petra agent (on the hardest difficulty). Although I don't find this as interesting as the other areas, it is a useful tool that could be nice to apply to other aspects of the game. Overall, I think it would be really exciting to be able to explore some of the research questions in 0 AD as I think it could be beneficial both to researchers but also would make it easier to incorporate the results of this research into 0 AD (making it an even better game!). Of course, this is only true if the functionality required to be added to 0 AD is easy to maintain and doesn't add overhead taking away from the development of the core game features and functionality. I am also hopeful that incorporating some of these machine learning capabilities could also be beneficial to the community and raise awareness of 0 AD! As far as technical requirements, I made an RPC interface for controlling the AI from Python (because the majority of machine learning tools are in Python). This makes it possible to explore 1, 2, and 3 as well as provides necessary functionality for 4, 5, and 6. As mentioned above, I have an example of #1 on GitHub and I think this could make for really interesting undergraduate projects (as well as potentially interesting integrations into the game). However, I think 0 AD is a particularly unique opportunity for exploration of 4 and 6. Game balancing (#4) still requires the ability to programmatically edit the unit parameters which I have explored a little bit but haven't added to the game. If this is something that others find interesting (and wouldn't mind me asking a few questions ), I would be open to adding this as well. Anyway, I find these machine learning problems and applications quite exciting both for 0 AD and for AI/ML research but I want to know what the rest of the community thinks! Let me know what you think or if you have any questions/comments! * I say *OpenAI gym-like* because a gym environment requires an observation space (numerical representation of the world for the AI), action space (numerical representation of the actions the AI can perform), and reward function to be defined. It isn't clear what the most appropriate choices for these would be (and they could vary based on the specific scenario) so I would prefer making more of a "meta-gym" where it is basically an OpenAI gym that needs the user to specify these values.