hi everyone thanks so much uh for coming to the next installment of the ucl dark invited speaker series today we're hearing from yandong chan on the topic of finding good representations for search and exploration in reinforcement learning uh juan dong is a research scientist and manager in facebook ai research working on deep reinforcement learning and representation learning he's the lead scientist and engineer for the elf open go and dark forest go projects prior to that he was in the google self-driving car team from 2013 to 2014 and prior to fight he received a phd from the robotics institute at carnegie mellon university uh in 2013. i'm really looking forward to what you have to say to us today around don so over to you yeah i think uh uh yeah everybody have already covered the majority of it yeah so my name is i'm from facebook i'm a designer and manager uh in fayette and also thanks very much for add and team to introducing me and invite me to be here to give a talk so today the topic is to find the guru presentation for search and exploration in reinforcement so let me start with the first slides and the first slide basically always show that oh yeah that's a great improved success for uh deep models now we have seen it for like a lot of different places uh so we see like uh in games right in reinforcement you see like with deep models uh the the performance getting super good and that's creating super performance superhuman performance in multiple games right and also in cv and nrp we see like uh the model that is being deployed is being substantially better than this pre-deep model deep learning uh era right so and uh behind all that i mean there's uh one thing that is actually changed right so between uh this uh uh before and after this deep learning era right so before different era what happens is people just say okay let's just use linear regression so within that regression uh you have features and you engineer features and then hopefully with one layer network and you want to use that feature to predict different outputs why right so what really changes after this deep learning era is we start to use deep models so what do we mean by deep models so we just stack like a bunch of layers and uh between the input outputs uh and then and hopefully like during this training then this intermediate layers we actually learn good representations uh so that this representation can be better used uh to predict uh the final output why right so that's basically like the the the thing like it's like once and summarize summarization to to summarize like what's happening before and after the different era so i think people might wonder like why that's the case right so there's one component which is called uh why why representation learning can actually help so we have venture of works but we're going to skip here because in this talk we're going to focus on uh representation learning in reinforcement learning and so in this topic there's a lot of papers that is on that direction but mostly they are focused on focusing on like several concrete topics right so the first topic is like what is the state representation right so in this topic we have seen like many papers so for example like this one is the very famous the museum paper that paper basically uh shows that if we can encode the current state of a game into a hidden state and you can use that hidden state to do search and you can do branching until you find the interesting groups policies to to to learn and you can basically train everything end to end and recently we also see we can actually learn the representation by combining the discrete and continuous loss functions and uh to uh in order to basically like uh uh have a good policy to master atari games this is from google deepmind uh sorry from google play so and we also see like a bunch of papers that can aim for like learn the action representation right so in that case uh there could be the case that there are too many actions and you want to find the representation so that you can deal with these actions in a kind of uniform manner and you might also be able to transfer uh from one action to uh from one space of action to another space of action so this you can be you can use that to transfer across different tasks so this is being used extensively in recommender systems in which uh you might have a million of items to choose from in that case you might need to use action balance in order to get this to work right and uh but in in this talk i'm going to talk a little bit about other things not uh state or actual representation but something that's more high level right so what i mean by high level is that you actually want to have representation for something that is bigger than this just stated action alone right so those are a few examples exactly uh from the previous uh papers for example you can use representation for the entire policy uh so that's actually one paper which is called parrots from uh dcs ikea that shows that you can actually use representation to represent the policy and the behavior policy and you can transfer them here for c for existing tasks to new tasks right so you can also representation of the environment so that you can deal with the new task learning etcetera so in this talk i'm going to cover these three components uh so we actually think about something that is actually different from all this and that comes with like nine nice interesting solutions for different objects that your p that happens in the reinforcement so the the talk will cover these three components the first one is how to learn representation of this entire action space you can actually change the action space in order to get a better performance when optimizing a function using this mdp formulation you can also find a different way to represent the value changes in perfect information game so that the search becomes much more efficient and also easier and finally we are i'm talking about a little bit about this ongoing work which is basically uh to study the representation changes uh for a reinforcement exploration and that's a very preliminary work we have like a few slides talking about the current progress okay so let's start with the first topic which is the representation uh for the action space so for this uh i'm not focusing on games but we are trying to think about more general optimization tasks so in these tasks we basically deal with a hard optimization problem and you want to find a way to use reinforcement learning to solve them right so by automation problems here what i mean is there's a bunch of like combinatorial optimization problems that we're going to deal with right so for example you have a objective function that to minimize the distance traveled for a salesman and then you basically want your salesman to travel uh every city once and only once and come back to the original point at the same time you want the distance to be minimized right so you can also think about the job scouting apartment et cetera so for all these optimization problems i mean they can be naturally formulated as mdp uh by uh specifically specify specifying where is the state and where is dash right so they can turn this entire thing into ndp right so but the interesting part of this kind of setting is there exists many mdps for a single optimization problem right so it could be the case that uh we don't need to model this operator problem as mdp at all we just say okay let's do end to end training right so the workshop prediction we gave in the permanent specification it's uh basically provide you with solution in one shot and you can also try other way of formulating it with different mdps you can say okay let's uh predict one part after another and here you get is the complete solution or you can say okay let's start with the initial guess of the solution and each step of the mdp becomes a refinement of this solution and two you cannot get this solution uh the better right so and finally you can even try to learn what is the best way to find the action space and so that this in time dp becomes easier to solve so so from all these examples you can actually see that representation actually really matters in terms of solving these optimization problems right so in the following few slides we're talking a little bit about the examples of all the uh other different uh kind of formulations so for directing direct predicting the solutions uh what you see is uh there's a lot of papers that starting from 2015 uh that try to use uh sequential sequence models uh that to predict directly the solution of the uh of the of those problems right so in these cases you specify like what is the solution you send them to uh signal sequence models and automatically it will give you the solution without any search and without any refinements right but these are actually like a first trials of us using uh machine learning for controlling racial problems right so after that people realize that i mean these problems are hard and you cannot just use that to solve it there's a lot of other way to formulate it so one way that we have been thinking about is the local rewriting framework in which we are trying to starting from a feasible solution and intuitively converge to a good solution so in this case we basically pick a local solution and then we pick a small component and then we use a a rule a bigger rule to change this small component so that that solution becomes better and then we repeat this process so we find that this actually this approach actually quite effective and we show that it's actually doing well compared to a bunch of other baselines so in different problems like online job scheduling express implications and also uh yeah vehicle routings so by the way the code is already online if people are interested you can take a look and this paper is being uh accepted in a lot is near europe's 2019. so and then we come back to this original question right so we have all this different way of formulating the action space and we usually fix them uh when we start with uh uh when we start with solving this optimization problems right so but the question becomes why we want to predefine the action space right so why not we actually learn the action space uh in this optimization of procedures right so this is the key difference between optimization problems and the mdps that is formulated by games right so for optimization problems we only care about the final solution we don't really care how we get it right so we actually actually leave additional room for us to learn the representation of the action space so that the form the optimization can be done better so this actually to actually show this is the case uh one example i i really like is this formula this this following example right so uh here basically want to show that okay what is the effects of representation of action space for the efficiency of the search accuracy right so here's like a one example in the architecture architecture search space so in which we have a 1 364 networks each with different depths a different number of channels and also different kernel sizes and the goal here is to find a network with the best accuracy using the fewest trials okay so i mean you definitely can do things like a random search or a trainer reinforcement agent whatever right so but here we want to emphasize okay what is the emphasize the importance of representation of the action space if you consider the two action spaces like the first one is the sequential one that is the normal one we usually think about right so okay uh here we have enough mental networks with different depths and different channels okay the second action will be okay we first add this first layer and the set is kernel size as well as the channel size and then we add another layer set as color size and turn on set etcetera until we say stop and the network is forming and we can use the network to train uh the uh to to to get this performance and then we get a number in terms of accuracy right so but we can also think about a different way of constructing this action space which is called the global action space so in that case we first set the depth of a network okay and then we set the kernel size as well as channel size at each layer of the network okay so we can actually see that i mean these two action spaces actually uh do uh perform much different uh in the uh in actual the the curves so this here is like a curve that shows that here the x-axis is number of samples and the number of networks you want to sample and evaluate and the y-axis is the the amount of accuracies right so how much the best accuracy could be after you already have a few hundred examples so ideally we want that curve to be go straight to the highest accuracy ever as soon as possible so this is basically the ideal case so we can see that i mean the global one is actually doing much better than the sequential ones that's actually very interesting i mean intuitively that makes sense because a human when human does all this decision making process human is not really think about details first right whom you always think okay maybe the depths of the network is the most important one so we want to make sure that the depth is being decided first okay we'll say all the depths of network is important so we are choosing like deep network first and then we figure out the details right so rather than starting from the details saying okay what is the kernel size and channel size of the first layer of network which doesn't really matter that much right so this is actually give you this kind of feeling that okay for optimization problems maybe this ordering or in some sense like what is the first most important decision to make is the important part rather than stick to a one mdp and then solve for this mdp which may not be ideal right so uh here uh we want to go one step further right so we instead of like choosing between uh multiple action spaces we might even think about like what is the is that a way to actually automatically learn uh this action space right so that's actually a very interesting uh component so what do i mean by learn action space this basically means that you if you think about this search tree uh in the uh in this game environment then what happens you actually change the semantics of the edges in this search tree right so which is not allowing games for sure because in games each action is well defined but it's doable in optimization because the optimization there's no well-defined way of uh search space uh of the action space and uh um yeah and and and state space and you can define by yourself okay so then like this basically introduce the first part of this talk that says like okay how can we learn the action space in this in the two recent papers that have been published in europe 2020 as well as tea party 2021 so we actually think about the way to learn the action space so here the action is defined as the partition of this search space right so suppose like all these architectures are in this small and this like huge search space then the partition uh the the action now becomes like how we should partition uh the search space so that uh the petition is in favor of a very efficient search so that's basically the the way to define this action space so what is the criteria for defining this action space so here's like a uh the the figure that you can visualize uh with these two different action uh space that the global one and the sequential as i mentioned before right so we can see that the global under the global action space what happens is the the search tree is kind of uh well aligned with the performance of the model right so in the global action space you can see that for all the some trees some branches actually contain like a very bad accuracy performance models and some other branches actually consider contain like a good models right so and about in the sequential action space there all this like with the band mode they mingle together uh into uh different branches of this of the tree so we don't we don't really want this sequential uh model splitting because this basically means that we have to go into every search tree branch in order to find uh the good uh models right so that's actually not good and this actually will waste us a lot of time and efforts and computational resources in order to get a good model what we really want is this global action space so that the good models are clustered around interes into the sub tree and the brand models are also in another ventral subtree so they can just prove them away you say okay this branches does not work at all then we should not spend too much time efforts and in order to search that branch right so this actually give us like the motivation also the criteria how we should uh partition the the space and also construct a action space so and then like this is like one example of how we can learn this action space uh t is like an example with this two-dimensional uh uh two-dimensional uh space so suppose the network is only parameterized by number of filters as well as the number of the depths right so in that case like there you're going to have got a bunch of samples and some samples have a high accuracy some person actually have a lower accuracy and then you can actually learn a linear classifier or whatever classifier you want in order to split like the the good ones versus from the bad ones and you can use that learned linear classifiers as uh the way to position the space into the good part of the space uh the the subspace and also the bad parts of the subluxions right so then you split this environment into your left and the right branches and the left one is basically saying okay i'm choosing to pick the good action space and the right one is your i'm choosing to pick the right the bad uh action space okay and then you can do this intuitively and until you form like a tree you can get this also in this into this hierarchical manner and uh you know we are not restricted to use uh a linear uh classifier or linear acquisition you can also use a non-linear boundary in order to separate the good from bad so the idea here is uh you want these good branches to be always on the left so that when you explore the space you actually will use that good uh you always focus yourself into these small good regions but at the same time you also have other branches that are not that great but you also want them in order to explore the space and you can pick some regions that are actually a promising in the in your later stage okay so uh given this component then we have this character this entire algorithm called the latent space main card research so in that uh approach what happens is you first use the initial random summon data to train the action space and then once you have this action space learned you search uh using this learn action space and here fixed number of laws that are being used so here we are using banking basically called research uh to do this uh kind of uh splitting and also you get like a more data so that you can go come back to this the first stage that you can train this action space okay so the reason why we're using country search exploration is also important because at the beginning you might be able to get trapped into local minima to say okay the left one is also good and you get like a bunch of samples that are reasonably good but not optimal while the actual optimal are very good solutions actually hiding into regions uh that are not that great right so maybe the majority of the parts here are really bad so that the this entire region appeared to be a bad region but it actually contained a very important and interesting part that is optimal solution so you still need some exploration so so you have to basically use some samples along that path to explore that subspace sub regions in order to keep finding like a good uh good good points uh for for for us to use in the future so now like we uh focus on like uh talk about the performance of this uh of these models right so uh i think uh we basically we i basically use that to to test whether it works well in like architecture search that's actually one of the first motivation at the beginning to see whether this approach is working so we actually construct a few data sets and we see that whether this approach is doing well in different data sets so what you see is that when we actually using a large and larger data set the performance of this approach compared to the other approaches like base optimization or random or or like other approach for many car for neural network architecture search is actually much better so uh you can actually see that when data cells become larger and until we reach to this naspash101 which contains 420k models it's already pre-trained on cypher10 dataset by google and we see that our approach actually is doing uh much better compared to the sr approaches in terms of this uh number of other accuracies right so how fast accuracy grows given the number of samples that you need in order to achieve better models okay so we also try this in open domains and we show that our approach can achieve pretty good turbulent errors with only like 800 number of trials okay so we also tried this approach in black box optimization in which you don't have an architect network but you only have a blackboard function you want to call right so and in that case like we can actually show that with this approach our same capacity is much higher sample efficiency is much higher uh compared to this existing solvers which is one is called tubule it just causes the base optimization and we see that with our approach uh the curve is the solid curve the solid curve is like drops much faster in terms of the function values because it's minimizing a function compared to other other cases so we also try this approach in optimizing and non-linear policies for mojoku tasks and we show that with if you have a lot of dimensions in this optimization problems i mean this approach actually do much better compared to the rest of this optimization problems okay so we also apply that to multi-objective optimization problems and recently we see this is also showing good performance compared to the previous approaches okay so this code is open source and if people are interested in using it feel free to click the link and and it should be easy to use you can simply define a function above our function and put it into this code and then it will basically give you a good solutions in short amount of time and our approach is being used in a third place any place team in last year's europe's 2020 black box officer competition okay so and then like uh here i'll come to the second part of the talk which is uh how can we find representations for easy search so for this uh um we're aiming for like a different uh uh different domain which is basically uh search in improved information games and so i think for this one uh so we first want to this is a basic one slide that shows the difference between perfect information games and imperfect information games so you basically show that the left hand is a tree that people often encounters in the perfect information games in which like there's basically no cycles and once you start from here you make one decisions you go into the left branch of the tree and then uh you can that branch of tree has nothing to do with this right branch of the tree etc right so that's basically like that what happens in the performation game and also what happens for uh people thinking about like search based approaches that's very clear but uh for improv information games things are a little bit different and so the the reason why this case is uh you might have two players right so player one here player two there and uh one players what is visible to one players may not be visible for other players and so because of that what happens is you might see like a these two nodes are very basically being spitting around for one player uh maybe actually is one node for another player right so this basically means that each of this uh each of this player each player actually sees like partial information and they may not be able to distinguish between them and make decisions here's like one example about this input information game and so for example you only have two cards right so you give like one private car to player one and the other player player a private car to player two so there are four possible possibilities right so zero zero all the way to one one right so and then you can say okay if you are basically the game designer you have complete formation then you actually can distinguish all these four possibilities right so and this will be the history of this game however if you think from the perspective of player one and player two they're actually seeing very different things right so from from the first from the perspective of player one you only see like two possible cases right so in the possible first cases is uh the player will only hold a card of zero the second case is the player only hold the card of what right so and uh you don't have to see any cases because the player does not really see the other people's player other people's card right so and also the same thing happens in the player two two cases right the player two only only see two kisses as well right so the the first case is like he is being getting the card of uh zero the second case is uh he gets a card of one right so you guys should basically you have this kind of this concept called this information set right so basically means that you can only have a policy that is consistent and also constant within this region which is the information set okay so because of that structure this is the reason why we actually see these circles here right so if you treat this information set as this a single node in the the search tree then what you see is that you're going to see the search tree as you have these kind of circles uh in this manner because what is distinguishable from player one may not be distinguishable from player two so you have this kind of like a weird structure in this imperfect information games and uh you're going to see more if you have more actions right so for example for that action it's supposed like player 1 has action a and b and for different a action a and b you're going to split this information set into two four different information sets corresponding to eight different complete information uh histories so this is the gaming condition that this action is public so that everyone can see it and everyone can use that action to split these two information sets from two to four right so this is actually uh makes things a little bit complicated and how can we actually do search is one of the very hard problems we need to face for this kind of situations and what makes things even harder is is that i mean for uh in many cases you cannot just optimizing just one node at a time because this will also lead to local optimum so this is actually like one problem in this uh uh collaborative improv information games right so here's like one thing for example that shows this is the case so suppose we have two agents who are communicating with each other and they are they can speak both french and english right but uh the the problem with uh this kind of communication is uh because they don't know each other's card or who is who is good at friends who is good at english so they may get like a fixed thing to uh local minimum right for example they are both speak french but actually there's one solution uh that is even better which is uh both are speaking english maybe that are better but none of this neither of this agent are able to switch unilaterally uh their policies in order to achieve better solution because if one of the agents actually switch the policy then the communication will break and then most of them will receive -1 are very likely rewards only if both of these agents switch their policies at the same time they are able to able to basically get like a much better uh solutions right so this actually makes things even more complicated because in order to get a good policies you actually need to find a good improvement for both of them at the same time so this can be basically represented by this very simple diagram that shows there's two information sets for these two players and they need to basically change their policies at the same time in order to achieve a better solution otherwise you get you might get to like the local national equipment okay so in order to achieve that uh there's like actually like a bunch of different ways of doing that right so one thing we can think about is that is a very nice formulation in which uh you might say okay how about uh we just pick a subtree and we don't do like local improvements right so and then like we might need to pick like multiple information sets in order to entertain their policies in order to jump out of the local minimum right so then the problem is like because of this complicated structure of this uh uh this game tree that is actually not a tree but it's a dag uh things have become like a kind of very complicated right so the reason why it's complicated is uh this is the following slides so the reason why this is the case is that you actually have a dependencies between different policies right so for example uh in imperfect information gain what happens is you might see there's a history that go out of this information set all the way and go back to this information set that is in the main stream that is the interchanging in this active information that you want to change their policies right so this actually makes things very complicated uh because uh in order to trace all these uh uh histories which is these black dots so that this in a trees like that the changes after you make changes to the policy you have to basically go back and trace like a weather all these histories are being visited and being like a used in all these information sets whose policies may have been changed this actually is complicated but unfortunately what what can we do is that if we change the representation uh for the uh overvalued changes of this uh uh of these values then we actually see like interesting very nice formulations so that that prevents you from tracking all these uh histories uh holidays like histories over this entire tree uh so basically like what we do in this paper is that uh we actually compose a policy chain density and that is actually has nice properties first of all the identity is if you add this density together of over all the possible histories in this entire game tree then this will naturally represent difference between uh the all policy gain values and the new policy given values and the second property is that for regions that process doesn't change in that region no matter where they are and the the the the density is actually goes to zero i saw for the this basically goes to zero no matter whether the upstream or downstream policy has changed so this actually these two properties actually is very nice if you put them together you actually will actually get like this new representation for value changes right so then the all value changes will be only depending on uh the the only the active information sets where the policies has been changed and then you sum them together and you get these overall value changes this is a very nice formulation so that you can actually do a much cleaner and search paradigm in order to find the better policies that are in improvement of executing policies and based on that we can actually discover this joint policy search algorithms and that search algorithm is basically doing uh this kind of that first search and they use that the previous formula to actually compute how much changes between an old policy and a new policy after this policy change and you can do this intuitively until the this approaches cannot find a better solution anymore okay so we basically apply that idea to multiple small-scale games and we can show that i mean if you combine this approach with the uh with existing like a policy iteration approaches and our approach is actually doing much better right so we actually see that it can improve existing policies and also help you to jump out of this local minima right so this is basically the performance after you apply this gps over the existing uh policies that is learned it is also found by uh the this uh by the existing like policy improvement approaches so we see that uh this number is acting better than the previous numbers and we can also even do like a sample based version of it uh but not on by basically uh only sample like the histories in each uh information set rather than you and all the possible information other possible histories okay and uh we can see that if the history if the number of samples you have sampled is basically uh it's basically like a uh above some threshold then we actually see the performance had improved and uh and sometimes like a sampled version is actually better than the full version because the sample version actually break the symmetry and it getting like even better performance finally we actually applied this approach to contracting bridge contrast bridge and bidding and we actually showed that this approach can is able to get a break this local optimal issues and able to reach like a higher performance so we are uh basically comparing against the w5 which is a champion of the computer bridge tournament in multiple years okay so finally uh i'm we'll spend like the last 15 minutes um that's the last part that is the representation uh for eye exploration so that part is basically like a i basically first talked about our paper which is called the b board and in this people paper we actually talk about propose like a new way of exploring the entire space of this of the reinforcement settings so basically in we actually have a very simple uh criteria uh for the arrival exploration and we show that that criteria is actually doing very well right so in many kind of cases so uh this criteria is very simple you basically say this is the intrinsic reward you want to get and the exclusive reward is basically composed of several components right so first of all you first have a chartered trace given a trade tree you first compute design these scores uh between these two nearby states so here you have st and st plus one and uh for so you first compute ind score for uh for this nodes you can put any score for that nodes and then you subtract a two and then you get the take the max of it and also this also multiplied by the episodic computation count so note that this i d score is actually computed as a random network institution so you actually have uh two networks so one is the online network and the other is the random fixed target network so uh then like you basically what you do is uh you uh you first train uh an online network five prime given the previous exiting states right so if you already visit that states then the the distance between these two networks output will be a small and which means that this is uh this shows that that particular state has already been visited right so if you already if you have explored to a region so that the expert states is uh uh the state has never been explored then what happens is that there will be a huge gap between the output of the online network and the render network so that these two difference will be become huge so this corresponds to the fact that the the the number of rotation counts is small so this is basically a way to uh to to basically show you actually have a better performance sorry the way you you show that you actually will have a lot of meditations uh in one state and there's some other states that you are not familiar with okay so in people like the idea here is that you basically want to uh you basically want to check the trailer trees and see is there any kind of gap between these mutation counts difference right so if you have already explored a space that is uh in this left part and this space is uh i mean this this space has been like a lot of rotation counts there and if you go outside and the retaining count will come small because there's a bottleneck here and naturally you are going to use these ingredients as a way to ver to to as like a way to uh giving like higher rewards in this spot for the disposal nodes right so you get higher rewards here because uh according to our criteria by this two this will be approximately the beta attention counts the inverse of retaining accounts for t plus one and this one will be the rotation counts for t if there's a huge gap between the two which means that there's both next state there and you basically assign them with higher rewards so that exploration will become easier uh to to achieve right so once you achieve the higher rewards and the agents will be attempt to getting to this place so once the agent actually already learned how to get to this bottleneck regions then the vacation counts of this bottleneck states will become higher and naturally this gap will diminish over time and then you're going to uh not focusing on that part and we'll be focusing on the second uh that the next bottleneck regions so that the exploration can go further okay so that's basically like the major idea of this people paper and you can actually see that we although like this paper is uh this idea is very simple it actually does a much good job in many uh situations so i think one of these interesting uh experiments that people have already seen and we already disconnected is the experiment on this mini grid so what we can see is uh we can see this approach actually does very well in many of these uh hard tasks in many green environments right so and uh we see that for all these 12 hot challenging environments and the people actually do much better than existing approaches including right and also amigo and we actually see that i mean all these people can actually solve all this situations within 120 million steps so here's like a bunch of figures that shows that i mean in heart especially in a hard task people actually does much better and able to solve them in uh in like less than a million uh environmental steps which is actually very interesting these are very simple uh uh criteria is actually doing a very uh very effective approach so we can also show that if we do pure exploration in mini grid using these people environment you'll just be build criteria compared to the ind criteria we see that these people criteria actually do much better in terms of exploring uh this entire environment while the ind might get stuck in this in a few rooms and then never can go back the problem with ind is that it's basically uh gets stuck with somewhere because uh once it gets familiar with some rooms uh then in my choose to go back to the previous rooms because the previous room appeared to be less familiar compared to the current room and then you're going to see this kind of back and forth phenomena using this ind framework but people actually was able to get rid over that and explore more rooms in a fewer number of environment steps okay and we also done an ablation study i'm showing like what are the uh so basically the important component for each of these design choices right so for example like the max is important and also this episodic uh rotation counts is also important in order to achieve good performance we also achieve we also apply this be both exploration tasks to net hack which is a nice environment conducted developed by a by fair london by team and also ad about that as you know that's a very nice environment to to work to work on and we show that uh people is actually doing well in multiple tasks of this environment and we also show that our approach is uh doing uh well in revenge without having a lot of interesting a lot of like handcrafted features okay but finally uh recently we also start to think about like uh how can we uh if we change the representation uh with b bolt then what will happen right so one interesting thing that we actually perceive is that bible is actually very sensitive to different kind of features used in the in the ind part right so if we use random features then we see that this is a nice curve going up right so for when if we have more environment steps however if we are using a different representation of the for the rotation counts then what happens here is that you're going to for example if you use this double uh by simulation control or you are using icm features then what happens is that you're going to see like a very bad performance it doesn't really go up at all right but if we use the successor representation then the performance actually drops uh it goes even faster uh with more uh with like uh uh with small environment steps so this is actually quite interesting so one thing we are suspecting here is uh it really depends on the quality of the representation of the representation if the representation can actually efficiently give you a nice hint and ideas about this environment right so then this representation is actually good so once you one thing we have already observed is uh for successor representation what happens is uh uh with uh the success orientation you're going to see the similar representation within each room but very different transition if you go between rooms so because of that interesting feature then it actually essentially can make this mdp from like a 10 to 10 maybe 10 to 10 like a grid back to maybe like a smaller md piece so that each of the states is approximately represents a room rather than a single cell so this actually gives a lot of convenience and freedom right so and also i can make this uh exploration much more efficient so that is actually a very nice part of the representation so we are still exploring towards this direction uh in order to find the the good representation in order to achieve even better performance uh for be bold okay so thanks for your attention and i will leave the rest 80 minutes for any questions Back To Top