Which Mutual Data Illustration Studying Goals are Ample for Management? – The Berkeley Synthetic Intelligence Analysis Weblog


Processing uncooked sensory inputs is essential for making use of deep RL algorithms to real-world issues.
For instance, autonomous automobiles should make selections about tips on how to drive safely given data flowing from cameras, radar, and microphones concerning the situations of the highway, site visitors indicators, and different automobiles and pedestrians.
Nevertheless, direct “end-to-end” RL that maps sensor information to actions (Determine 1, left) will be very troublesome as a result of the inputs are high-dimensional, noisy, and include redundant data.
As an alternative, the problem is commonly damaged down into two issues (Determine 1, proper): (1) extract a illustration of the sensory inputs that retains solely the related data, and (2) carry out RL with these representations of the inputs because the system state.

Determine 1. Illustration studying can extract compact representations of states for RL.

All kinds of algorithms have been proposed to study lossy state representations in an unsupervised style (see this latest tutorial for an outline).
Not too long ago, contrastive studying strategies have confirmed efficient on RL benchmarks reminiscent of Atari and DMControl (Oord et al. 2018, Stooke et al. 2020, Schwarzer et al. 2021), in addition to for real-world robotic studying (Zhan et al.).
Whereas we might ask which aims are higher through which circumstances, there may be an much more primary query at hand: are the representations discovered by way of these strategies assured to be ample for management?
In different phrases, do they suffice to study the optimum coverage, or would possibly they discard some vital data, making it unimaginable to resolve the management downside?
For instance, within the self-driving automobile state of affairs, if the illustration discards the state of stoplights, the car can be unable to drive safely.
Surprisingly, we discover that some broadly used aims usually are not ample, and actually do discard data that could be wanted for downstream duties.

Defining the Sufficiency of a State Illustration

As launched above, a state illustration is a perform of the uncooked sensory inputs that discards irrelevant and redundant data.
Formally, we outline a state illustration $phi_Z$ as a stochastic mapping from the unique state area $mathcal{S}$ (the uncooked inputs from all of the automobile’s sensors) to a illustration area $mathcal{Z}$: $p(Z | S=s)$.
In our evaluation, we assume that the unique state $mathcal{S}$ is Markovian, so every state illustration is a perform of solely the present state.
We depict the illustration studying downside as a graphical mannequin in Determine 2.

Determine 2. The illustration studying downside in RL as a graphical mannequin.

We’ll say {that a} illustration is ample whether it is assured that an RL algorithm utilizing that illustration can study the optimum coverage.
We make use of a consequence from Li et al. 2006, which proves that if a state illustration is able to representing the optimum $Q$-function, then $Q$-learning run with that illustration as enter is assured to converge to the identical answer as within the authentic MDP (should you’re , see Theorem 4 in that paper).
So to check if a illustration is ample, we will examine if it is ready to characterize the optimum $Q$-function.
Since we assume we don’t have entry to a process reward throughout illustration studying, to name a illustration ample we require that it could possibly characterize the optimum $Q$-functions for all attainable reward features within the given MDP.

Analyzing Representations discovered by way of MI Maximization

Now that we’ve established how we’ll consider representations, let’s flip to the strategies of studying them.
As talked about above, we intention to check the favored class of contrastive studying strategies.
These strategies can largely be understood as maximizing a mutual data (MI) goal involving states and actions.
To simplify the evaluation, we analyze illustration studying in isolation from the opposite features of RL by assuming the existence of an offline dataset on which to carry out illustration studying.
This paradigm of offline illustration studying adopted by on-line RL is turning into more and more well-liked, notably in functions reminiscent of robotics the place amassing information is onerous (Zhan et al. 2020, Kipf et al. 2020).
Our query is due to this fact whether or not the target is ample by itself, not as an auxiliary goal for RL.
We assume the dataset has full help on the state area, which will be assured by an epsilon-greedy exploration coverage, for instance.
An goal might have a couple of maximizing illustration, so we name a illustration studying goal ample if all the representations that maximize that goal are ample.
We’ll analyze three consultant aims from the literature when it comes to sufficiency.

Representations Realized by Maximizing “Ahead Data”

We start with an goal that appears prone to retain a substantial amount of state data within the illustration.
It’s carefully associated to studying a ahead dynamics mannequin in latent illustration area, and to strategies proposed in prior works (Nachum et al. 2018, Shu et al. 2020, Schwarzer et al. 2021): $J_{fwd} = I(Z_{t+1}; Z_t, A_t)$.
Intuitively, this goal seeks a illustration through which the present state and motion are maximally informative of the illustration of the following state.
Due to this fact, every part predictable within the authentic state $mathcal{S}$ needs to be preserved in $mathcal{Z}$, since this is able to maximize the MI.
Formalizing this instinct, we’re capable of show that every one representations discovered by way of this goal are assured to be ample (see the proof of Proposition 1 within the paper).

Whereas reassuring that $J_{fwd}$ is ample, it’s price noting that any state data that’s temporally correlated will likely be retained in representations discovered by way of this goal, regardless of how irrelevant to the duty.
For instance, within the driving state of affairs, objects within the agent’s visual field that aren’t on the highway or sidewalk would all be represented, regardless that they’re irrelevant to driving.
Is there one other goal that may study ample however lossier representations?

Representations Realized by Maximizing “Inverse Data”

Subsequent, we take into account what we time period an “inverse data” goal: $J_{inv} = I(Z_{t+ok}; A_t | Z_t)$.
One method to maximize this goal is by studying an inverse dynamics mannequin – predicting the motion given the present and subsequent state – and plenty of prior works have employed a model of this goal (Agrawal et al. 2016, Gregor et al. 2016, Zhang et al. 2018 to call a couple of).
Intuitively, this goal is interesting as a result of it preserves all of the state data that the agent can affect with its actions.
It due to this fact might seem to be candidate for a ample goal that discards extra data than $J_{fwd}$.
Nevertheless, we will truly assemble a practical state of affairs through which a illustration that maximizes this goal isn’t ample.

For instance, take into account the MDP proven on the left facet of Determine 4 through which an autonomous car is approaching a site visitors mild.
The agent has two actions obtainable, cease or go.
The reward for following site visitors guidelines is determined by the colour of the stoplight, and is denoted by a crimson X (low reward) and inexperienced examine mark (excessive reward).
On the correct facet of the determine, we present a state illustration through which the colour of the stoplight isn’t represented within the two states on the left; they’re aliased and represented as a single state.
This illustration isn’t ample, since from the aliased state it’s not clear whether or not the agent ought to “cease” or “go” to obtain the reward.
Nevertheless, $J_{inv}$ is maximized as a result of the motion taken remains to be precisely predictable given every pair of states.
In different phrases, the agent has no management over the stoplight, so representing it doesn’t improve MI.
Since $J_{inv}$ is maximized by this inadequate illustration, we will conclude that the target isn’t ample.

Determine 4. Counterexample proving the insufficiency of $J_{inv}$.

Because the reward is determined by the stoplight, maybe we will treatment the problem by moreover requiring the illustration to be able to predicting the fast reward at every state.
Nevertheless, that is nonetheless not sufficient to ensure sufficiency – the illustration on the correct facet of Determine 4 remains to be a counterexample because the aliased states have the identical reward.
The crux of the issue is that representing the motion that connects two states isn’t sufficient to have the ability to select the very best motion.
Nonetheless, whereas $J_{inv}$ is inadequate within the basic case, it could be revealing to characterize the set of MDPs for which $J_{inv}$ will be confirmed to be ample.
We see this as an attention-grabbing future path.

Representations Realized by Maximizing “State Data”

The ultimate goal we take into account resembles $J_{fwd}$ however omits the motion: $J_{state} = I(Z_t; Z_{t+1})$ (see Oord et al. 2018, Anand et al. 2019, Stooke et al. 2020).
Does omitting the motion from the MI goal influence its sufficiency?
It seems the reply is sure.
The instinct is that maximizing this goal can yield inadequate representations that alias states whose transition distributions differ solely with respect to the motion.
For instance, take into account a state of affairs of a automobile navigating to a metropolis, depicted under in Determine 5.
There are 4 states from which the automobile can take actions “flip proper” or “flip left.”
The optimum coverage takes first a left flip, then a proper flip, or vice versa.
Now take into account the state illustration proven on the correct that aliases $s_2$ and $s_3$ right into a single state we’ll name $z$.
If we assume the coverage distribution is uniform over left and proper turns (an affordable state of affairs for a driving dataset collected with an exploration coverage), then this illustration maximizes $J_{state}$.
Nevertheless, it could possibly’t characterize the optimum coverage as a result of the agent doesn’t know whether or not to go proper or left from $z$.

Determine 5. Counterexample proving the insufficiency of $J_{state}$.

Can Sufficiency Matter in Deep RL?

To know whether or not the sufficiency of state representations can matter in observe, we carry out easy proof-of-concept experiments with deep RL brokers and picture observations. To separate illustration studying from RL, we first optimize every illustration studying goal on a dataset of offline information, (just like the protocol in Stooke et al. 2020). We gather the fastened datasets utilizing a random coverage, which is ample to cowl the state area in our environments. We then freeze the weights of the state encoder discovered within the first section and practice RL brokers with the illustration as state enter (see Determine 6).

Determine 6. Experimental setup for evaluating discovered representations.

We experiment with a easy online game MDP that has an identical attribute to the self-driving automobile instance described earlier. On this recreation known as catcher, from the PyGame suite, the agent controls a paddle that it could possibly transfer backwards and forwards to catch fruit that falls from the highest of the display (see Determine 7). A optimistic reward is given when the fruit is caught and a destructive reward when the fruit isn’t caught. The episode terminates after one piece of fruit falls. Analogous to the self-driving instance, the agent doesn’t management the place of the fruit, and so a illustration that maximizes $J_{inv}$ would possibly discard that data. Nevertheless, representing the fruit is essential to acquiring reward, because the agent should transfer the paddle beneath the fruit to catch it. We study representations with $J_{inv}$ and $J_{fwd}$, optimizing $J_{fwd}$ with noise contrastive estimation (NCE), and $J_{inv}$ by coaching an inverse mannequin by way of most chance. (For brevity, we omit experiments with $J_{state}$ on this submit – please see the paper!) To pick out probably the most compressed illustration from amongst those who maximize every goal, we apply an data bottleneck of the shape $min I(Z; S)$. We additionally examine to working RL from scratch with the picture inputs, which we name “end-to-end.” For the RL algorithm, we use the Smooth Actor-Critic algorithm.

Determine 7. (left) Depiction of the catcher recreation. (center) Efficiency of RL brokers skilled with completely different state representations. (proper) Accuracy of reconstructing floor reality state components from discovered representations.

We observe in Determine 7 (center) that certainly the illustration skilled to maximise $J_{inv}$ leads to RL brokers that converge slower and to a decrease asymptotic anticipated return. To higher perceive what data the illustration accommodates, we then try to study a neural community decoder from the discovered illustration to the place of the falling fruit. We report the imply error achieved by every illustration in Determine 7 (proper). The illustration discovered by $J_{inv}$ incurs a excessive error, indicating that the fruit isn’t exactly captured by the illustration, whereas the illustration discovered by $J_{fwd}$ incurs low error.

Rising remark complexity with visible distractors

To make the illustration studying downside tougher, we repeat this experiment with visible distractors added to the agent’s observations. We randomly generate photos of 10 circles of various colours and change the background of the sport with these photos (see Determine 8, left, for instance observations). As within the earlier experiment, we plot the efficiency of an RL agent skilled with the frozen illustration as enter (Determine 8, center), in addition to the error of decoding true state components from the illustration (Determine 8, proper). The distinction in efficiency between ample ($J_{fwd}$) and inadequate ($J_{inv}$) aims is much more pronounced on this setting than within the plain background setting. With extra data current within the remark within the type of the distractors, inadequate aims that don’t optimize for representing all of the required state data could also be “distracted” by representing the background objects as an alternative, leading to low efficiency. On this tougher case, end-to-end RL from photos fails to make any progress on the duty, demonstrating the problem of end-to-end RL.

Determine 8. (left) Instance agent observations with distractors. (center) Efficiency of RL brokers skilled with completely different state representations. (proper) Accuracy of reconstructing floor reality state components from state representations.


These outcomes spotlight an vital open downside: how can we design illustration studying aims that yield representations which might be each as lossy as attainable and nonetheless ample for the duties at hand?
With out additional assumptions on the MDP construction or information of the reward perform, is it attainable to design an goal that yields ample representations which might be lossier than these discovered by $J_{fwd}$?
Can we characterize the set of MDPs for which inadequate aims $J_{inv}$ and $J_{state}$ can be ample?
Additional, extending the proposed framework to partially noticed issues can be extra reflective of lifelike functions. On this setting, analyzing generative fashions reminiscent of VAEs when it comes to sufficiency is an attention-grabbing downside. Prior work has proven that maximizing the ELBO alone can’t management the content material of the discovered illustration (e.g., Alemi et al. 2018). We conjecture that the zero-distortion maximizer of the ELBO can be ample, whereas different options needn’t be. General, we hope that our proposed framework can drive analysis in designing higher algorithms for unsupervised illustration studying for RL.

This submit is predicated on the paper Which Mutual Data Illustration Studying Goals are Ample for Management?, to be introduced at Neurips 2021. Thanks to Sergey Levine and Abhishek Gupta for his or her beneficial suggestions on this weblog submit.