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Mobile Game Reviews: Let's Talk Teon

NimbleThor

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In this daily blog (and video)-series I take a first impressions look at the best mobile games that I come by. Be sure to share your favorite mobile game with the rest of us in the comments below!

Teon is an open-world MMO so hardcore that it might not sit well with everyone. To give but two examples, you have to tap your armor in the correct order in order to equip it; starting from shirt to armor and then cloak, and secondly, there's no minimap and you have to read quest descriptions and navigate using N, E, S, W accordingly! 

Running around in the map was a bit too slowly-paced for my taste, but the game plays very well overall, and is surely very unique! 

Monetization happens through a $10 per month subscription to access certain members-only areas (think Runescape). Nothing like anything I've seen on mobile yet.

My thoughts on Teon:


Google Play: https://play.google.com/store/apps/details?id=com.lakoo.teonworld&hl=en
iOS: https://itunes.apple.com/hk/app/teon/id1093023243?mt=8

Subscribe on YouTube for more commentaries: https://goo.gl/xKhGjh
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Or Twitter: https://twitter.com/nimblethor



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Haha yea that wasent pvp bro 

I played it since day one and it's awesome game, only non p2w I found that's good.

Atm it ain't to big but it grows everyday!

I'm one of the strongest and that wasent pvp hehe and there is so much to do and accomplish in game 

Here's a video when I play at higher lvl :)

https://youtu.be/NPg7-i9xik0

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      Download the project in ZIP format directly from GitHub
       

      Level 1 developer: Traditional asset management
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      Performance is pretty good, which is nothing surprising considering the scope of the project. You could literally turn this project into a VR experience and I assure you that your users will not fill any of the bile buckets that so many players filled when playing Eve: Valkyrie.

      In our case, we will be focusing on the memory section. The simple view mode will display something as depicted below:
      Level 1 Asset Management - Simple Memory Profiling
      The numbers on the texture size look pretty oversized for just displaying a single skybox at any given time, don't you agree? Surprise incoming: this is the pattern you will find in many unoptimized projects you are likely to suffer lead. But heck, in this case it's just a collection of skyboxes. In others, it will be more about characters, planets, sounds, music. You name it, I have it.
      If dealing with many assets falls under your responsibility, well, then I am glad you are reading this article. I will help you transitioning to scalable solution.
      Time for magic. Let's switch the memory profiler to detail mode. Have a look!
      Level 1 Asset Management - Detailed Memory Profiling
      Holy crap, what happened there? All skybox textures are loaded in memory, but only one is displayed at any given time. You see what we did? This rookie architecture produced the whoooooooping figure of 400mb.
      This is definitely a no-go, considering this is just a small piece of a future game. Addressing this very problem is the foundation for our next section.
      Come with me!
      Summary:
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      In video- games you start at level 1, which is great, but once you know the gameplay rules it is time to leave the safe city walls in our quest to level up. That is exactly what this section is about.
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      Unity Package Manager - Unity Addressables
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      Level 2 Asset Management (Unity Addressables) - Main Window
      My dear reader: that was the easy part. Now comes the fun part.
      I want you to pay a visit to an old friend of ours from the previous section: Sir Manager. If you check it, you will notice it is still holding direct references to our assets! We don't want that.
      We are teaching our manager to use indirect references instead - i.e. AssetReference (in Unreal Engine you might know them as soft references).
      Let us do just that, let's beautify our component:
      using UnityEngine; using UnityEngine.AddressableAssets; using UnityEngine.ResourceManagement.AsyncOperations; public class Manager : MonoBehaviour { [SerializeField] private List<AssetReference> _skyboxMaterials; private AsyncOperationHandle _currentSkyboxMaterialOperationHandle; public void SetSkybox(int skyboxIndex) { StartCoroutine(SetSkyboxInternal(skyboxIndex)); } private IEnumerator SetSkyboxInternal(int skyboxIndex) { if (_currentSkyboxMaterialOperationHandle.IsValid()) { Addressables.Release(_currentSkyboxMaterialOperationHandle); } var skyboxMaterialReference = _skyboxMaterials[skyboxIndex]; _currentSkyboxMaterialOperationHandle = skyboxMaterialReference.LoadAssetAsync(); yield return _currentSkyboxMaterialOperationHandle; RenderSettings.skybox = _currentSkyboxMaterialOperationHandle.Result; } } What happens here is the following:
      A major change happens in line 7, where we hold a list of indirect references (AssetReference) instead of direct material references. This change is key, because these materials will NOT be loaded automatically just by being referenced. Their loading will have to be made explicit. Afterwards, please reassign the field in the editor. Line 13: since we are now in an asynchronous workflow, we favor the use of a coroutine. We simply start a new coroutine that will handle the skybox material change. We check in lines 18-20 whether we have an existing handle to a skybox material and, if so, we release the skybox we were previously rendering.  Every time we do such a load operation with the Addressables API, we receive a handle we should store for future operations. A handle is just a data structure containing data relevant to the management of a specific addressable asset. We resolve specific addressable reference to a skybox material in line 23 and then you call its LoadAssetAsync function, over which you can yield (line 25) so we wait for this operation to finish before proceeding further. Thanks to the usage of generics, there's no need for sucky casts. Neat! Finally, once the material and its dependencies have been loaded, we proceed to change the skybox of the scene in line 26. The material will be offered in the Result field that belongs to the handle we used to load it. Level 2 Asset Management (Unity Addressables) - AssetReference list
      Keep in mind: this code is not production-ready. Do not use it when programming an airplane. I decided to favor simplicity over robustness to keep the matter simple enough.
      Enough with explanations. It is time you and I saw this in action.
      If you would be so kind to perform the following steps:
      In the addressables window, cook the content (build player content) Then make a build on a platform of your choice Run it and connect the (memory) profiler to it. Protect your jaw from dropping.
      Level 2 (Unity Addressables) - Build Player Content
        Level 2 Asset Management (Unity Addressables) - Memory Profiler
          Isn't asset cooking delicious?
      I like happy profilers. And what you saw is the happiest profiler the world has ever seen. A satisfied profiler will mean several things. For one, it means happy players playing your game in a Nokia 3210. It also means happy producers. And as of you, it means a happy wallet.
      This is the power of the Addressables system.
      Addressables which comes with little overhead on the team. On the one side, programmers will have to support asynchronous workflows (easy-peasy with Coroutines). Also, designers will have to learn the possibilities of the system, e.g. addressable groups, and gather experience to make intelligent decisions. Finally, IT will be delighted to set up an infrastructure to deliver the assets over the network, if you opt to host them online.
      I have to congratulate you. Let me tell you what we have accomplished:
      Appropriate memory management. Faster initial loading times. Faster install times, reduced in-store app size. Higher device compatibility. Asynchronous architecture. Opening the door of storing this content online → decoupling data from code. I would be proud of such a gain. It's for sure a good return on investment.
      Oh, and make sure to mention your experience with Addressables in job interviews.
       
      INTERMEDIATE: Instancing and reference counting. Read on it my blog post for information on this topic.
      OPTIONAL: Alternative loading strategies. Read on it my blog post for information on this topic.
       
      Summary:
      Addressables-based asset management scales just well Addressables introduces asynchronous behavior Do not forget to cook content on changes or you'll give your game a pinkish tint!  

      Level 3 Asset Management (??) - Content Network Delivery
        Level 3 Asset Management (??) - Content Network Delivery
      In the previous section, we achieved the biggest bang for the buck. We leveled up our skills by moving from a traditional asset management system to an addressables-based workflow. This is a huge win for your project, as a small time investment gave your project the room to better scale in assets while keeping your memory usage low. That accomplishment indeed made you step up to level 2, congrats! However, one question is yet to answer:
      Is that it?
      No. We barely scratched the surface of Addressables, there are further ways to improve your project with this game-changer package.
      Of course, you do not have to memorize all the details regarding Addressables, but I highly suggest you to have an overview of them because down the road you are likely to encounter further challenges and you will be thankful to have read a bit further. That's why I prepared an extra short guide for you.
      There you will learn about the following aspects:
      The Addressables window: the details matter Addressables profiling: don't leak a (memory) leak ruin your day Network delivery: reduce the time-to-play user experience Build pipeline integration Practical strategies: speed up your workflow, trash your 10-minute coffee breaks  
      And more importantly, answer questions such as:
      What is the hidden meaning behind Send Profiler Events? How useful is the AddressableAssetSettings API? How do I integrate this all with the BuildPlayerWindow API? What's the difference between Fast Mode, Virtual Mode and Packed Mode? In order to grab the level 3 guide check out my blog post
    • By Ey-Lord
      Hello everyone

       
      I am here to gather your opinion, remarks, ideas or any constructive criticism you may have about what I am going to present. Don’t be shy!


       
      A bit of background:

      I am working alone on an indy web-based game, a simulation of RPG (idle game) where the player controls a group of 4 characters that he can sent into battle and that will fight automatically based on some AI preference that are similar to the FF 12 system (but more complex / powerful). He then earns some experience and resources that he can use to improve his unit’s gear, talents and skills. He has a lot of control on what skills his characters will use and how/when.


       
      What brings me here today:

      The AI of Monsters. I have the AI settings for players covered (basically a bunch of if/then/and/or/else settings that he can combine and order so that his units will act as he intends in battle). I’ve been working on the AI of monsters for quite some time, made a long break and came back recently to it.


       
      Short description of the battle system:

      No movement involved. Battle is fully automated. Players setup its units AI settings before battle and monsters are controlled by a separate AI. This is a 4v4 battle, like FF7 with some kind of ATB and any time a unit fill its ATB, it can play and the then the next unit who will fill it will play, etc. The player is completely free of his playstyle and may create very offensive group or very defensive ones. 4 healers or 4 tanks is completely possible.

      The battle system is very complex and allows for very varied and sometimes unusual strategies, like killing your own allies to proc an “on death buff” that will be devastating for the opponent.


       
      What I want for my AI?

      It needs to be fun to fight against and challenging. Ideally, I would like an AI as smart as possible (not omniscient but thinking as a human would). I know that a super-smart AI is not always the best way to make a game fun or challenging but in the context of my game, this is the result I want to achieve. It may seem unfair to have the AI try to kill your squishy while your tank is standing right there but my class design gives the tools to players to counter that so it’s not an issue (tanks are not purely aggro based for example). I want players to always be challenged by AI moves and that they must carefully think about their strategy because if they leave a big hole in it, I want the AI to exploit it.

      In practice, it means a few requirements:

      No dumb decision / do not fall into obvious player’s traps Exploit obvious flaws of the opponent Act in coordination when appropriate with other units Able to find who should be their focus in the player’s team (some notion of threat) Find the best move to use and if there is some kind of combo possible, use it

      These requirements are harder to meet than it looks. The issue is the sheer number of different mechanisms and strategies available to players and to monsters as well. For example, there are many cases where killing or attacking a player unit might be detrimental (units that return damages or that gain power when you hit then for example).


       
      What I have tried before?

      I have tried or at least reviewed many different AI concepts so far.

      -          A simple copy of my player’s AI system (hierarchical if/then/else). It was easy to script as I already have the UI in place for players so I can quickly define a basic AI for any new monster’s group. The main drawbacks are that it needs to be written for every monster group, it does not allow smart targeting and cannot find the best target or the best skill to use. It will also make dumbs decision as the targeting options cannot assess threats at all.

                I’ve rules out planners since for purely selecting the best pair of (skill, target), they do not seem to match my needs.           (H)FSM or BT does not seems to match my needs as monsters do not have states / transition condition that can lead to something useful for me.        I’ve ruled out aNNs as they might, with proper training, be able to find the best action at a given time but it’s very tedious to implement and will not solve my need of finding combo or coordinating with other units very well. (plus, let’s be honest, I’d be a bit out of my depth to program them)           I have spent an extensive period of time trying with tree searches. Mainly: monte-carlo with random sampling and came to the conclusion that due to the complexity of my battle system, it is excessively costly to compute any kind of reliable data this way.
      -        My current AI system is a version of my first one (the same as the players) but with access to some “smarter” targeting function that in theory allow to choose the best target. These functions work by gathering data for thousands of simulated fights during the AI time to play (1 second). It’s a first step to find the best target but not very accurate (lots of big flaws that can be exploited by players) and it is very time consuming and that is something I’m trying to get away from. I do not want to use 100% of the players CPU as I do now.


       
      What is my latest idea?

      I started to study more in-depth the Utility theory as described by Dave Marks (I read his book and watched his GDC AI lectures as well). I liked the idea. I like that I can start on something relatively simple and add more considerations as things progress to handle more and more situations. While my work began as something very close to utility theory, it evolved a bit afterward. Here is what I plan on doing to compute a unit’s best course of action:


       
      A – Score every of its move (each move is a pair [skill, target]).

      B – Chose the move according to a selection strategy (highest score, weighted random, random amongst the top scores… lots of different selection algorithm can be used there).


       
      So far, easy, right? Let’s dig deeper into our first phase of scoring (A), which is the hard part. For all the damage or healing skills:


      Step 1: The final scoring of the move [skill,target] will be function of the a “Survival” scoring for the player team and for the enemy team. An example of this relationship could be: Adding all the survival scores of each unit in Team A and divide the result by the addition of all the survival scores for each unit in team B.

      Step 2: The survival score of each unit will be its Health after the move we are evaluating, divided by the total damage per turn that we estimate other units can deal to her (minus the total heal it ca receive). [This a step where we can process damage and heal over time as well]

      Step 3: This damage per turn estimation will be, initially, the sum for every unit in battle of the damage or heal per second it can deal to that unit. For example: If I’m alone vs 2 bad guy that can deal 1 dmg/turn and if I can deal 1 heal/turn, the damage per turn estimation against me will be 2-1 = 1. [This is not optimal since we are counting the damage of each unit once per enemy unit but it’s a start]

      Step 4: To compute the DPS or HPS of each unit, we review the unit’s skills and compute their output against the unit we want to evaluate it against. From that, we construct a skill sequence to maximize the damage output and once we got the optimal skill sequence, we can compute its DPS or HPS output and pass it along for Step 3.


       
      It might seem like a lot of work, since, in a world with only damage or healing skills, the DPS or HPS sequence of each unit will be the same in every situation and as such only the damage done or healing done by the skill evaluated would be enough. But…


       
      The tricky part comes from buffs and debuffs. If we use the above algorithm, (de)buffs that changes the damage or healing someone does or receive will be evaluated correctly as it will change the damage or heal per second output of units and it would affect the survival score and the final scoring. That is why I chose to include DPS and HPS computations for each unit for each move.


       
      This is all fine until we consider (de)buffs that changes the power of other (de)buffs. Like: I cast a buff that double the length of all my future buffs. My algorithm can’t evaluate it correctly. It’s a situation that will be common enough in my game and I want my AI to deal with it. Note: there are more complex situations where a unit could buff a buff that buffs a buff that buff a buff [….] that will end-up buffing a damage or healing skills, but those cases will not be addressed as they will hopefully be rare and too cumbersome to compute anyway.


       
      So, my goal is to score properly buffs that: 

            Buffs the damage or healing output of someone           Buffs that buffs a skill that does the above

       
      L    Long story short of how I am doing that. I’m using my initial algorithm but while also estimating damage or healing per second change for each dps or hps sequence.To do that I’m evaluating every move of the unit (or every unit in case of AoE but lets keep it simple with single target) that is targeted by the buff. So, we are switching PoV here compared to the initial unit we are evaluating (unless the move evaluated is buffing itself)

      -          I’m doing the above in 2 situations:

      o   A : After a cast of the buff skill I’m evaluating

      o   B : Without the cast of the buff, just like if it was that unit’s turn to play

      -          Using a sort of min/max approach: if the unit targeted by the buff is an ally, we will take the best branch of our tree in A and compare it with the same branch (pair [skill,target]) in B. If the unit targeted by the buff is an enemy, we want to lower their maximum score and will select the tree branch that does that in A to also compare it with the same branch in B.

      -          The information we extract here are DPS or HPS delta for each sequence of DPS/HPS for each unit vs each other unit.

      -          Then, we go back to our steps 1 to 5 and compute our scoring for the move (buff) while using our new dps/hps deltas to get better and more accurate dps/hps sequence for units affected by the buff.


       
      This is basically it. I’ve ran a manual version of the algorithm in 2 different battle settings to test it and see if it gave good results. It worked. Not flawlessly but it worked. Lots of cases will still require tweak and additions to the basic idea but I think its promising. (taunts and CCs are not easy to deal with but it’s manageable)


       
      What I like is that I can add more considerations later (as in the utility theory) like: resource cost, general unit strategy (cleave or focus), behavior (careful, lunatic, reckless). While this will still be a bit time consuming it should be a good order of magnitude faster than my current AI. It also does not prevent me from adding hardcoded AI move if I want to “script” more some monsters. Debugging and tweaking might be a bit painful though, especially when fights will involve lots of skills & stats but that’s an issue that most AI for my game would likely have anyway.


       
      To come back with my initial goals:

              No dumb decision / do not fall into obvious player’s traps
      o   Not perfect but it should choose the best target whenever possible

                 Exploit obvious flaws of the opponent
      o   Same as above

              Act in coordination when appropriate with other units
      o   This can be done simply by adding weight to some targets or computing moves for all units of a group before deciding which one to take (for example to take the best move vs a specific unit, on average)

             Able to find who should be their focus in the player’s team (some notion of threat)
      o   It will naturally focus the unit who is the easiest to kill and debuff or CC the ones that deal the more heal/damage. But, to better solve this, we will need to add other considerations to the AI scoring process, It should not be *too* hard    

            Find the best move to use and if there is some kind of combo possible, use it
      o   Combo are very often in the form of buff/debuff setup before an actual damaging or healing skills and my AI can compute up to a 3 moves combo (buff > buff > skill that dmg or heal) which should cover most cases.


       
      I’m quite happy with my initial tests. I’m not going to be coding it now. My goal was to reflect on the subject on paper and try to see if designing my AI would be a roadblock or not for my project. There are a few other area I want to design and take time to really think about before getting back to my project full time. I’d love to hear your toughs and feedbacks about my AI ideas. Do you see huge roadblocks I’m missing? Does it sound ok to you?

      If you read that far…. thank you and I can"t wait to hear from you guys😊

    • By G-Dot
      Hello everyone! I've decided to implement a destructible enemies system.
       
      Description:
      When bullet hit enemy in specific part of his body(arm fo example) armour, which covers that part of body, will fall off. Enemies in my game are robots so this means that when shooting them certain plates of their armour will fall off. All enemies will have a different amount of armour plates
       
      My solution: 
      The only solution I came up with is to make an actor with a static mesh and attach it to the bones of enemy's skeletal mesh. When bullet hit that actor it detaches and fly away with add impulse node.

       
      Question: 
      Maybe there is a better solution, which I'm missing and it's more efficient. 
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