Paper:
We present an approach for learning state representations in multi-task reinforcement learning. Our method learns multiple low-dimensional state representations from raw observations in an unsupervised fashion, without any knowledge of which task is executed, nor of the number of tasks involved. The method is based on a gated neural network architecture, trained with an extension of the learning with robotic priors objective. In simulated experiments, we show that our method is able to learn better state representations for reinforcement learning, and we analyze why and when it manages to do so.
1 INTRODUCTION
(a) (b)
Figure 1: Slot car racing – the agent has learn how to drive any of the cars as far as possible (left), based on its raw observations (right).
In many reinforcement learning problems, the agent has to solve a variety of different tasks to fulfill its overall goal. A common approach to this problem is to learn a single policy for the whole problem, and leave the decomposition of the problem into subtasks to the learner. In many cases, this approach is successful (Mnih et al., 2015; Zahavy et al., 2016), but it comes at the expense of requiring large amounts of training data. Alternatively, multiple policies dedicated to different subtasks can be learned. This, however, requires prior knowledge about how the overal problem decomposes into subtasks. More-
over, it can run into the same issue of requiring large amounts of data, because the subtasks might overlap and thus afford shared computation to solve them.
A common approach to address overlapping problems is multi-task learning (Caruana, 1997): by learning a single policy with different subgoals, knowledge between the different tasks can be transferred. This not only allows to learn a compact representation more efficiently, but also improves the agent’s performance on all the individual subtasks (Rusu et al., 2016).
Multi-task learning, however, faces two problems: it requires the decomposition of the overall problem into subtasks to be given. Moreover, it is not applicable if the subtasks are unrelated, and are better solved without sharing computation. In this case, the single-policy approach results in an agent that does not perform well on any of the individual tasks (Stulp et al., 2014) or that unlearns
1The first two authors contributed equally to this work.
the successful strategy for one subtasks once it switches to another one, an issue known as catastrophic forgetting (McCloskey & Cohen, 1989).
In this work, we address the problem of identifying and isolating individual unrelated subtasks, and learning multiple separate policies in an unsupervised way. To that end, we present MT-LRP, an algorithm for learning state representations for multiple tasks by learning with robotic priors. MT-LRP is able to acquire different low-dimensional state representations for multiple tasks in an unsupervised fashion. Importantly, MT-LRP does not require knowledge about which task is executed at a given time or about the number of tasks involved. The representations learned with MT-LRP enable the use of standard reinforcement learning methods to compute effective policies from few data.
As explained before, our approach is orthogonal to the classical multi-task learning approach, and constitutes a problem of its own right due to the issues of underperformance and catastrophic forgetting. Therefore, we disregard the shared knowledge problem in this paper. However, any complete reinforcement learning system will need to combine both flavors of multi-task learning, for related and unrelated tasks, and future work will have to address the two problems together.
MT-LRP is implemented as two neural networks, coupled by a gating mechanism (Sigaud et al., 2015; Droniou et al., 2015) as illustrated in Figure 2. The first network, χ , detects which task is being executed and selects the corresponding state representation. The second network, ϕ , learns task-specific state representations. The networks are trained simultaneously using the robotic priors learning objective (Jonschkowski & Brock, 2015), exploiting physics-based prior knowledge about how states, actions, and rewards relate to each other. Both networks learn from raw sensor data, without supervision and solely based on the robot’s experiences.
In a simulated experimental scenario, we show that MT-LRP is able to learn multiple state representations and task detectors from raw observations and that these representations allow to learn better policies from fewer data when compared with other methods. Moreover, we analyze the contribution to this result of each the method’s individual components.
2 RELATED WORK
MT-LRP combines three ideas into a novel approach for task discovery and state representation learning: 1) extracting state representations for each task with robotic priors (Jonschkowski & Brock, 2015); 2) discovering discrete tasks and corresponding actions/policies in a RL context (Stulp et al., 2014; Höfer & Brock, 2016); 3) using gated networks to implement a “mixture of experts” (Jacobs et al., 1991; Droniou et al., 2015).
State Representation Learning: Learning from raw observations is considered a holy grail in reinforcement learning (RL). Deep RL has had major success in this, using model-free (Mnih et al., 2015) but also by combining model-free and model-based RL (Levine et al., 2015). These approaches apply end-to-end learning to get from raw input to value functions and policies. A different approach is to explicitly learn state representations using unsupervised learning, e.g. using auto-encoders (Lange et al., 2012). Recently, Watter et al. (2015) extended this idea to learn state representations jointly with dynamic models and apply optimal control to compute a policy. We use learning with robotic priors (Jonschkowski & Brock, 2015), a state representation learning method
that exploits information about temporal structure, actions, and rewards. We go beyond previous work by not only learning single state representations, but learning multiple state representations given raw data from multiple tasks.
Options and Parameterized Skills: A common approach to factorizing a RL problem into subtasks are macro-actions, often called options (Sutton et al., 1999; Hengst, 2002). The main difference with our approach is that options are used to hierarchically decompose one high-level task into subtasks (and learn sub-policies for these subtasks), whereas we learn task-specific state representations for different high-level tasks. However, options bear resemblance on a technical level, since they are often implemented by a high-level “selection” policy that parametrizes low-level policies (Daniel et al., 2012; Kupcsik et al., 2013; Stulp et al., 2014). Continuous versions of options, referred to as parametrized skills, have been proposed, too (Da Silva et al., 2012; Deisenroth et al., 2014; DoshiVelez & Konidaris, 2016). However, in all the work above, the state representation is given. To the best of our knowledge, state representation learning has not yet been considered in the context of RL with options or parameterized skills.
Gated Networks for Mixtures of Experts and Submanifold Learning: Gated networks are networks that contain gating connections, in which the outputs of at least two neurons are multiplied (Sigaud et al., 2015). This allows a gating neuron g to prohibit (or limit) the flow of information from one neuron x to another neuron y, similar to how transistors function. An early example of gated networks is the mixture of experts approach (Jacobs et al., 1991; Jacobs & Jordan, 1993; Haruno et al., 2001), where separate networks in a modular neural network specialize in predicting subsets of training examples from a database. Our contribution is to extend mixtures of experts by state representation learning (e.g. from raw images) and to the more difficult RL (rather than supervised learning) context. Our gated network architecture is similar to the one proposed by Droniou et al. (2015). Their network simultaneously learns discrete classes jointly with continuous class variations (called submanifolds) in an unsupervised way, e.g., discrete digit classes and shape variations within each class. We use a similar architecture, but in a different way: rather than learning discrete classes, we learn discrete tasks; class-specific submanifolds correspond to task-specific state representations; and finally, we consider a RL rather than an unsupervised learning context.
As mentioned in the introduction, our work is orthogonal to multi-task learning (Caruana, 1997) which has been extensively studied in recent reinforcement learning literature, too (Parisotto et al., 2016). Our approach can be trivially combined with multi-task learning by by prepending the gate and state extraction modules with a subnetwork that shares knowledge across tasks. Another interesting multi-task approach is policy distillation (Rusu et al., 2016). This method combines different policies for multiple tasks into a single network, which enables to share information between tasks and to learn a compact network that can even outperform the individual policies.
3 BACKGROUND: STATE REPRESENTATION LEARNING FOR REINFORCEMENT LEARNING
We formulate MT-LRP in a reinforcement learning (RL) setting using a Markov decision process (MDP) (S,A,T,R,γ): Based on the current state s ∈ S, the agent chooses and executes an action a ∈ A, obtains a new state s′ ∈ S (according to the transition function T ) and collects a reward r ∈ R. The agent’s goal is to learn a policy π : S→ A that maximizes the expected return IE(∑∞t=0 γ trt), with rt being the reward collected at time t and 0 < γ ≤ 1 the discount factor. We consider an episodic setting with episodes of finite length, a continuous state space S and a discrete action space A.
In this work, we assume that the agent cannot directly observe the state s but only has access to observations o ∈ O, which are usually high-dimensional and contain task-irrelevant distractors. This requires us to extract the state from the observations by learning an observation-state-mapping ϕ : O→ S, and use the resulting state representation S to solve the RL problem (assuming that a Markov state can be extracted from a single observation). To learn the state representation, we apply learning with robotic priors (Jonschkowski & Brock (2015), from now on referred to as LRP). This method learns ϕ from a set of temporally ordered experiences D = {(ot ,at ,rt)}dt=1 by optimizing the following loss:
LRP(D,ϕ) = ωtLtemp.(D,ϕ)+ωpLprop.(D,ϕ)+ωcLcaus.(D,ϕ)+ωrLrep.(D,ϕ). (1)
This loss consists of four terms, each expressing a different prior about suitable state representations for robot RL. We optimize it using gradient descent, assuming ϕ to be differentiable. We now explain the four robotic prior loss terms in Eq. (1).
Temporal Coherence enforces states to change gradually over time (Wiskott & Sejnowski, 2002): Ltemp.(D,ϕ) = IE [ ‖∆st‖2 ] ,
where ∆st = st+1− st denotes the state change. (To increase readability we replace ϕ(o) by s.) Proportionality expresses the prior that the same action should change the state by the same magnitude, irrespective of time and the location in the state space:
Lprop.(D,ϕ) = IE [ (‖∆st2‖−‖∆st1‖) 2 ∣∣∣ at1 = at2].
Causality enforces two states st1 ,st2 to be dissimilar if executing the same action in st1 generates a different reward than in st2 .
Lcaus.(D,ϕ) = IE [ e−‖st2−st1‖ 2 ∣∣∣ at1 = at2 ,rt1+1 6= rt2+1].
Repeatability requires actions to have repeatable effects by enforcing that the same action produces a similar state change in similar states:
Lrep.(D, ϕ̂) = IE [ e−‖st2−st1‖ 2‖∆st2 −∆st1‖ 2 ∣∣∣ at1 = at2].
Additionally, the method enforces simplicity by requiring s to be low-dimensional.
Note that learning with robotic priors only makes use of the actions a, rewards r, and temporal information t during optimization, but not at test time for computing ϕ(o) = s. Using a, r and t in this way is an instance of the learning with side information paradigm (Jonschkowski et al., 2015).
4 MULTI-TASK STATE REPRESENTATIONS: MT-LRP
Now consider a scenario in which an agent is learning multiple distinct tasks. For each task τ ∈ {1, . . . ,T}, the agent now requires a task-specific policy πτ : Sτ → A. We approach the problem by learning a task-specific state representation ϕτ : O→ Sτ for each policy, and a task detector χ which determines the task, given the current observation. We will consider a probabilistic task-detector χ : O→ [0,1]T that assigns a probability to each task being active. In order to solve the full multi-task RL problem, we must learn χ, {ϕτ}τ∈{1,...,T} and {πτ}τ∈{1,...,T}. We propose to address this problem by MT-LRP, a method that jointly learns χ and {ϕτ}τ∈{1,...,T} from raw observations, actions, and rewards. MT-LRP then uses the state representations {ϕτ} to learn task-specific policies {πτ}τ∈{1,...,T} (using standard RL methods), and switches between them using the task detector χ . To solve the joint learning problem, MT-LRP generalizes LRP (Jonschkowski & Brock, 2015) in the following regards: (i) we replace the linear observation-statemapping from the original method with a gated neural network, where the gates act as task detectors that switch between different task-specific observation-state-mappings; (ii) we extend the list of robotic priors by the prior of task coherence, which allows us to train multiple task-specific state representations without any specification (or labels) of tasks and states.
4.1 GATED NEURAL NETWORK ARCHITECTURE
We use a gated neural network architecture as shown schematically in Fig. 2. The key idea is that both the task detector χ as well as the state representation ϕ are computed from raw inputs. However, the output of the task detector gates the output of the state representation. Effectively, this means the output of χ(o) decides which task-specific state representation ϕτ is passed further to the policy, which is also gated by the output of χ(o).
Formally, χ(o) = σ(χpre(o)) is composed of a function χpre with T -dimensional output and a softmax σ(z) = e z j
∑k ezk . The softmax ensures that χ computes a proper probability distribution over tasks.
The probabilities are then used to gate ϕ . To do this, we decompose ϕ into a pre-gating function
ϕpre that extracts features shared across all tasks (i.e. ”multi-task” in the sense of Caruana (1997), unless set to the identity), and a T ×M×N gating tensor G that encodes the T (linear) observationstate mappings (M = dim(s) and N is the output dimension of ϕpre). The value of the state’s i-th dimension si computes as the expectation of the dot product of gating tensor and ϕpre(o) over the task probabilities χ(o):
si = ϕi(o) = T
∑ k=1 χk(o) 〈Gk,i,:,ϕpre(o)〉. (2)
4.2 LEARNING OBJECTIVE
To train the network, we extend the robotic prior loss LRP (Eq. 1), by a task-coherence prior Lτ :
L= LRP(D,ϕ)+ωτLτ(D,χ), (3)
where ωτ is a scalar weight balancing the influence of the additional loss term. Task coherence is the assumption that a task only changes between training episodes, not within the same episode. It does not presuppose any knowledge about the number of tasks or the task presented in an episode, but it exploits the fact that task switching weakly correlates with training episodes. Moreover, this assumption only needs to hold during training: since χ operates directly on the observation o, it can in principle switch the task at every point in time during execution. Task-coherence applies directly to the output of the task detector, χ(o), and consists of two terms:
Lcon+sepτ = Lconτ +L sep τ . (4)
The first term enforces task consistency during an episode: Lconτ = IE [ H(χ(ot1),χ(ot2)) ∣∣∣ episodet1 = episodet2], (5) where H denotes the cross-entropy H(p,q) = −∑x p(x) logq(x). It can be viewed as a measure of dissimilarity between probability distributions p and q. We use it to penalize χ if it assigns different task distributions to inputs ot1 , ot2 that belong to the same episode. Note that task-consistency can be viewed as a temporal coherence prior on the task level (Wiskott & Sejnowski, 2002).
The second term expresses task separation and encourages χ to assign tasks to different episodes: Lsepτ = IE [ e−H(χ(ot1 ),χ(ot2 )) ∣∣∣ episodet1 6= episodet2]. (6) This loss is complementary to task consistency, as it penalizes χ if it assigns similar task distributions to ot1 , ot2 from different episodes. Note that L sep τ will in general not become zero. The reason is that the number of episodes usually exceeds the number of tasks, and therefore two observations from different episodes sometimes do belong to the same task. We will evaluate the contribution of each of the two terms to learning success in Section 5.2.
5 EXPERIMENTS
We evaluate MT-LRP in two scenarios. In the multi-task slot-car racing scenario (inspired by Lange et al. (2012)), we apply MT-LRP to a linearly solvable problem, allowing us to easily inspect what and how MT-LRP learns. In slot-car racing, the agent controls one of multiple cars (Figure 1), with the goal of traversing the circuit as fast as possible without leaving the track due to speeding in curves. However, the agent does not know a priori which car it controls, and only receives the raw visual signal as input. Additionally, uncontrolled cars driving at random velocity, act as visual distractors. We turn this scenario into a multi-task problem in which the agent must learn to control each car, where controlling the different cars corresponds to separate tasks. We will now provide the technical details of our experimental set-up.
5.1 EXPERIMENTAL SET-UP: SLOT-CAR RACING
The agent controls the velocity of one car (see Fig. 1), receives a reward proportional to the car’s velocity, chosen from [0.01, 0.02, . . . , 0.1], and a negative reward of −10 if the car goes too fast
in curves. The velocity is subject to Gaussian noise (zero mean, standard deviation 10%) of the commanded velocity. All cars move on independent lanes and do not influence each other. The agent observes the scenario by getting a downscaled 16x16 RGB top-down view (dimension N = 16×16×3 = 768) of the car circuit (Fig. 1(b)). In our experiments, there are two or three cars on the track, and the agent controls a different one in every episode. To recognize the task, the agent must be able to extract a visual cue from the observation which correlates with the task. We study two types of visual cues: Static Visual Cue: The arrangement of cars stays the same in all episodes and a static visual cue (a picture of the controlled car) in the top-left image corner indicates which car is currently controlled. Dynamic Visual Cue: The agent always controls the same car (with a certain color), but in each task the car is located on a different lane (as in Fig. 1(b)).
Data Collection and Learning Procedure: The agent collects 40 episodes per task, each episode consisting of 100 steps. To select an action in each step, the agent performs ε-greedy exploration by picking a random action with probability ε = 0.3 and the best action according to its current policy otherwise. The agent computes a policy after every τ episodes, by first learning the observation-state mapping ϕ (state representation) and then computing policies π1, . . . ,πτ (based on the outcomes of the learned χ and ϕ). To monitor the agent’s learning progress, we measure the average reward the agent attains on T test episodes, i.e. one test episode of length 100 per task (using the greedy policy), amounting to 8000 experiences in total. To collect sufficient statistics, the whole experiment is repeated 10 times. Policy Learning: We consider the model-free setting with continuous states S, discrete actions A and solve it using nearest-neighbor Q-learning kNN-TD-RL (Martı́n H et al., 2009) with k = 10. More recent approaches to model-free RL would be equally applicable (Mnih et al., 2015). Learning Strategies and Baselines: We compare five strategies. We run a) MT-LRP with 5 gate units (two/three more than necessary), state dimensionality M = 2 and using Lcon+sepτ as taskcoherence prior. We compare MT-LRP to several state representation methods; for each method we evaluate different M and report only the best performing M: a) robotic priors without gated network, LRP (M = 4), b) principal components analysis (PCA) on the observations (M = 20) and c) raw observations (M = 768). Additionally, we evaluate d) a lower baseline in the form of a randomly moving agent and e) an upper baseline by applying RL on the known 2D-position of the slot car under control (M = 2). We use the same RL algorithm for all methods. To learn the state representations with robotic priors, we base our implementation on Theano and lasagne, using the Adam optimizer with learning rate 0.005, batch size 100, Glorot’s weight initialization and ωt = 1,ωp = 5,ωc = 1,ωr = 5,ωτ = 10. Moreover, we apply an L1 regularization of 0.001 on ϕ . Additionally, we analyze the contribution of task coherence priors by applying MT-LRP to the full set of 8000 experiences a) without task-coherence, b) with task consistency Lconτ only c) with task separation Lconτ only) and d) without task consistency and separation L con+sep τ .
5.2 RESULTS
We will now present the three main results of our experiments: (i) we show that MT-LRP enables the agent to extract better representations for RL; (ii) we provide insight in how the learner detects the task and encodes the state representations; and finally, (iii) we show the contribution of each of the task-coherence loss terms.
MT-LRP Extracts Better State Representations for RL Figure 3 shows the learning curves for RL based on state representations learned by the different methods in the two-slot-car scenario (static visual cue on the left, dynamic on the right). No method reaches the performance of the upper baseline, mainly due to aliasing errors resulting from the low image resolution.
The random baseline ranges around an average reward of −84.9 with standard error 0.72 and was omitted from the Figure. The state representation learning baselines without robotic priors perform poorly because they are unable to identify the taskirrelevant distractions. MT-LRP gets very close to the performance of the upper baseline, especially for very low amounts of training data (d < 2500), whereas LRP does not even attain this level of performance for the full training set d = 8000 in the static task. The gap between MT-LRP and LRP increases even more if we add another car (Figure 5) because LRP can only learn one state representation for all three tasks. Including the three slot cars in this representation results in distractions for the RL method. However, in the dynamic-visual-cue scenario LRP-4 performs on par with MT-LRP. Surprisingly, running LRP with only two dimensions suffices to achieve the performance of MT-LRP. We
will explain this phenomenon below. To conclude, MT-LRP allows to learn as good or better policies than the baselines in all slot-car scenarios.
MT-LRP Detects All Tasks and Learns Good State Representations To gain more insight into what is learned, we analyze the state representations extracted by MT-LRP and LRP. Figure 4 shows the state representation learned by MT-LRP for the static-visual-cue scenario. Each point in the figure corresponds to one observation, markers indicate the task and colors the most active gate unit. We see that the first gate unit (blue) is always active for task 1 (circle), and the second gate unit for task 2. This shows that the task is detected with high accuracy. The task detector χ is also highly cer-
tain which is reflected in the fact that its entropy evaluated on the data is close to zero. Moreover, the states reflect the circular structure of the slot car racing track. We thus conclude that MT-LRP has learned to identify the tasks and to represent the position of each car on the track.
The RL experiments raised the question why LRP manages to solve the dynamic, but not the static-visual-cue scenario as well as MT-LRP. We hypothesize that, for the dynamic cue, LRP is able to extract the position of the car on regardless of which lane it is in using a single linear mapping. Figure 6 confirms this hypothesis: LRP filters for the car’s color (blue) along the track and assigns increasing weights to these pixels which results in the extraction of its position. It also assigns constant weights along the track in the red channel using the lane change of the two cars as an offset. This results in a mapping to two circles similar to Fig. 4, where the state encodes both the position and the task. Such a mapping can be expressed by a linear
function precisely because the features that are relevant for one task do not reappear in another task (e.g. a blue slot car in track 1 does not appear in the task where the blue car is in track 2).
However, there exists no equivalent linear mapping for the static-visual-cue variant of the slotcar problem, because cars that are relevant for one task are also present in every other task.
We can generalize from this insight as follows. A single linear observation-state-mapping is sufficient for multiple tasks if the state representation for every task can be extracted by a linear function using only features that stay constant for all other tasks. If this is the case, than there is no need for decoupling the extraction of task and state.
Task-Consistency is Critical for Learning Performance To understand the influence of
the different task-coherence prior variants, we compared their performance in Figure 7. We see that relying solely on the robotic priors gives poor results, mainly because the gate units are not used properly: more than one gate unit is activated per task (χ has high entropy). Adding the taskseparation prior forces the network to use as many gates as possible (5 in our case), leading to bad state representations. Interestingly, using task consistency only gives roughly the same result as using task consistency and task separation.
Discussion The experiments showed that MT-LRP is able to solve the representation and reinforcement learning tasks better than the baselines. Important questions for future work concern: the necessity and influence of the task-separation loss, in particular for short episode lengths and if the number of expected tasks exceeds the number of actual tasks; and transferring knowledge by adding a shared neural network layers before gating.
6 CONCLUSION
We have presented MT-LRP, a method for multi-task state representation learning with robotic priors. The method learns in an unsupervised fashion, solely based on the robots own observations, actions, and rewards. Our experiments confirmed that MT-LRP is effective in simultaneously identifying tasks and learning task-specific state representations. This capability is beneficial for scaling reinforcement learning to realistic scenarios that require dedicated skills for different tasks.
ACKNOWLEDGMENTS
We gratefully acknowledge the funding provided by the German Research Foundation (DFG, Exploration Challenge, BR 2248/3-1), the Alexander von Humboldt foundation through an Alexander von Humboldt professorship (funded by the German Federal Ministry of Education and Research). Additionally, Antonin Raffin was supported by an Erasmus+ grant.

Review:
This paper builds upon the method of Jonschkowski & Brock to learn state representations for multiple tasks, rather than a single task. The research direction of learning representations for multiple tasks is an interesting one, and largely unexplored. The approach in the paper is to learn a different representation for each task, and a different policy for each task, where the task is detected automatically and built into the neural network.
The authors state that the proposed method is orthogonal to multi-task learning, though the end goal of learning to solve multiple tasks is the same. It would be interesting and helpful to see more discussion on this point in the paper, as discussed in the pre-review question phase. References to other multi-task learning works, e.g. policy distillation and actor-mimic (both ICLR ’16), may be appropriate as well.
The method proposes to jointly learn a task classifier with a state representation learner, by using a differentiable gating mechanism to control the flow of information. The paper proposes a task coherence prior for this gating mechanism to ensure that the learned task classifier is temporally coherent. Introducing this structure is what enables the method to improve performance over the standard, non-multitask approach.
The evaluation involves two toy experimental scenarios. The first involves controlling one of two cars to drive around a track. In this task, detecting the “task” is very easy, and the learned state representation is linear in the observation. The paper evaluates the performance of the policies learned with the proposed approach, and shows sufficient comparisons to demonstrate the usefulness of the approach over a standard non-multitask set-up.
In the second navigation scenario, only the state representation is qualitatively shown, not the resulting control policy nor any other learned state representations for comparison. Since the multi-task state representation learning approach is only useful if you can also learn control better, the paper should also evaluate on control, with the same comparisons as in the first experiment. Without this evaluation, the experiment is incomplete.
Lastly, to be on par with publications at a venue like ICLR, the method should be evaluated more thoroughly, on a wider range of set-ups, to demonstrate the generality of the approach and show that the method applies to more complex tasks. While in theory, the method should scale, the experiments do not demonstrate that it can handle more realistic scenarios, such as scaling beyond MNIST-level images, to 3D or real images, or higher-dimensional control tasks. Evaluating the method in this more complex scenario is important, because unexpected issues can come up when trying to scale. If scaling-up is straight-forward, then running this experiment (and including it in the paper) should be straight-forward.
In summary, here are the pros and cons of this paper:
Cons
- The approach does not necessarily share information across tasks for better learning, and requires learning a different policy for each task
- Only one experimental set-up that evaluates learned policy with multi-task state representation
- No experiments on more realistic scenarios, such 3D environments or high-dimensional control problems
Pros: 
- This approach enables using the same network for multiple tasks, which is often not true for transfer and multi-task learning approaches
- Novel way to learn a single policy for multiple tasks, including a task coherence prior which ensures that the task classification is meaningful
- Experimentally validated on two toy tasks. One task shows improvement over baseline approaches
Thus, my rating would be higher if the paper included an evaluation of the control policy for navigation and included another more challenging and compelling scenario.
Lastly, here are some minor comments/questions on how I think the paper could be improved, but are not as important as the above:
Approach:
Could this approach be combined with other state representation learning approaches? e.g. approaches that use an autoencoder.
Experiments:
One additional useful comparison would be to evaluate performance in the single-task setting (e.g. only controlling the red car), as an upper bound on how well the policy should be able to perform. Does the learned multi-task policy reach the same level of performance? This upper bound will be tighter than the “known car position” baseline (which is also useful in its own right).
Does the “observations” baseline eventually reach the performance of the LRP approach? It would be useful to know if this approach simply speeds up learning (significantly) or if it enables better performance.
If there are aliasing issues with the images, why not just use higher resolution images?