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 find out how to drive safely given info flowing from cameras, radar, and microphones concerning the situations of the street, site visitors alerts, and different automobiles and pedestrians.
Nonetheless, 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 comprise redundant info.
As a substitute, 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 info, 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 vogue (see this current tutorial for an summary).
Lately, 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 may ask which targets are higher during which circumstances, there’s an much more primary query at hand: are the representations realized through these strategies assured to be enough for management?
In different phrases, do they suffice to study the optimum coverage, or may they discard some vital info, making it not possible to unravel the management downside?
For instance, within the self-driving automotive state of affairs, if the illustration discards the state of stoplights, the automobile can be unable to drive safely.
Surprisingly, we discover that some extensively used targets usually are not enough, and actually do discard info which may be wanted for downstream duties.

Defining the Sufficiency of a State Illustration

As launched above, a state illustration is a operate of the uncooked sensory inputs that discards irrelevant and redundant info.
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 automotive’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 operate 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 are going to say {that a} illustration is enough 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 (in the event you’re , see Theorem 4 in that paper).
So to check if a illustration is enough, we are able to 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 enough we require that it could characterize the optimum $Q$-functions for all attainable reward features within the given MDP.

Analyzing Representations realized through MI Maximization

Now that we’ve established how we are going to 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 info (MI) goal involving states and actions.
To simplify the evaluation, we analyze illustration studying in isolation from the opposite facets 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 changing into more and more in style, significantly in purposes reminiscent of robotics the place gathering information is onerous (Zhan et al. 2020, Kipf et al. 2020).
Our query is subsequently whether or not the target is enough 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 multiple maximizing illustration, so we name a illustration studying goal enough if all the representations that maximize that goal are enough.
We are going to analyze three consultant targets 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 info within the illustration.
It’s intently 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 during which the present state and motion are maximally informative of the illustration of the following state.
Subsequently, all the pieces predictable within the authentic state $mathcal{S}$ needs to be preserved in $mathcal{Z}$, since this might maximize the MI.
Formalizing this instinct, we’re in a position to show that every one representations realized through this goal are assured to be enough (see the proof of Proposition 1 within the paper).

Whereas reassuring that $J_{fwd}$ is enough, it’s value noting that any state info that’s temporally correlated will likely be retained in representations realized through this goal, irrespective of how irrelevant to the duty.
For instance, within the driving state of affairs, objects within the agent’s sight view that aren’t on the street or sidewalk would all be represented, despite the fact that they’re irrelevant to driving.
Is there one other goal that may study enough however lossier representations?

Representations Realized by Maximizing “Inverse Data”

Subsequent, we think about what we time period an “inverse info” goal: $J_{inv} = I(Z_{t+ok}; A_t | Z_t)$.
One technique to maximize this goal is by studying an inverse dynamics mannequin – predicting the motion given the present and subsequent state – and lots 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 info that the agent can affect with its actions.
It subsequently might appear to be a superb candidate for a enough goal that discards extra info than $J_{fwd}$.
Nonetheless, we are able to truly assemble a sensible state of affairs during which a illustration that maximizes this goal shouldn’t be enough.

For instance, think about the MDP proven on the left aspect of Determine 4 during which an autonomous automobile is approaching a site visitors gentle.
The agent has two actions accessible, cease or go.
The reward for following site visitors guidelines depends upon the colour of the stoplight, and is denoted by a purple X (low reward) and inexperienced examine mark (excessive reward).
On the precise aspect of the determine, we present a state illustration during which the colour of the stoplight shouldn’t be represented within the two states on the left; they’re aliased and represented as a single state.
This illustration shouldn’t be enough, since from the aliased state it’s not clear whether or not the agent ought to “cease” or “go” to obtain the reward.
Nonetheless, $J_{inv}$ 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 enhance MI.
Since $J_{inv}$ is maximized by this inadequate illustration, we are able to conclude that the target shouldn’t be enough.

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

For the reason that reward depends upon the stoplight, maybe we are able to treatment the problem by moreover requiring the illustration to be able to predicting the fast reward at every state.
Nonetheless, that is nonetheless not sufficient to ensure sufficiency – the illustration on the precise aspect 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 shouldn’t be sufficient to have the ability to select the very best motion.
Nonetheless, whereas $J_{inv}$ is inadequate within the normal case, it will be revealing to characterize the set of MDPs for which $J_{inv}$ will be confirmed to be enough.
We see this as an attention-grabbing future route.

Representations Realized by Maximizing “State Data”

The ultimate goal we think about 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 impression 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, think about a state of affairs of a automotive navigating to a metropolis, depicted beneath in Determine 5.
There are 4 states from which the automotive can take actions “flip proper” or “flip left.”
The optimum coverage takes first a left flip, then a proper flip, or vice versa.
Now think about the state illustration proven on the precise 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 inexpensive state of affairs for a driving dataset collected with an exploration coverage), then this illustration maximizes $J_{state}$.
Nonetheless, it could’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, (much like the protocol in Stooke et al. 2020). We accumulate the fastened datasets utilizing a random coverage, which is enough to cowl the state area in our environments. We then freeze the weights of the state encoder realized within the first part and prepare RL brokers with the illustration as state enter (see Determine 6).

Determine 6. Experimental setup for evaluating realized representations.

We experiment with a easy online game MDP that has an identical attribute to the self-driving automotive instance described earlier. On this recreation referred to as catcher, from the PyGame suite, the agent controls a paddle that it could transfer backwards and forwards to catch fruit that falls from the highest of the display screen (see Determine 7). A optimistic reward is given when the fruit is caught and a detrimental reward when the fruit shouldn’t be 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 $J_{inv}$ may discard that info. Nonetheless, 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 through most probability. (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 people who maximize every goal, we apply an info bottleneck of the shape $min I(Z; S)$. We additionally evaluate to working RL from scratch with the picture inputs, which we name “end-to-end.” For the RL algorithm, we use the Mushy 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 parts from realized 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 info the illustration incorporates, we then try to study a neural community decoder from the realized illustration to the place of the falling fruit. We report the imply error achieved by every illustration in Determine 7 (proper). The illustration realized by $J_{inv}$ incurs a excessive error, indicating that the fruit shouldn’t be exactly captured by the illustration, whereas the illustration realized by $J_{fwd}$ incurs low error.

Growing statement complexity with visible distractors

To make the illustration studying downside more difficult, we repeat this experiment with visible distractors added to the agent’s observations. We randomly generate photographs of 10 circles of various colours and substitute the background of the sport with these photographs (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 parts from the illustration (Determine 8, proper). The distinction in efficiency between enough ($J_{fwd}$) and inadequate ($J_{inv}$) targets is much more pronounced on this setting than within the plain background setting. With extra info current within the statement within the type of the distractors, inadequate targets that don’t optimize for representing all of the required state info could also be “distracted” by representing the background objects as an alternative, leading to low efficiency. On this more difficult case, end-to-end RL from photographs 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 parts from state representations.


These outcomes spotlight an vital open downside: how can we design illustration studying targets that yield representations which can be each as lossy as attainable and nonetheless enough for the duties at hand?
With out additional assumptions on the MDP construction or data of the reward operate, is it attainable to design an goal that yields enough representations which can be lossier than these realized by $J_{fwd}$?
Can we characterize the set of MDPs for which inadequate targets $J_{inv}$ and $J_{state}$ can be enough?
Additional, extending the proposed framework to partially noticed issues can be extra reflective of life like purposes. 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 not management the content material of the realized illustration (e.g., Alemi et al. 2018). We conjecture that the zero-distortion maximizer of the ELBO can be enough, whereas different options needn’t be. Total, 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 offered at Neurips 2021. Thanks to Sergey Levine and Abhishek Gupta for his or her invaluable suggestions on this weblog submit.