Epistemic POMDPs and Implicit Partial Observability – The Berkeley Synthetic Intelligence Analysis Weblog


Many experimental works have noticed that generalization in deep RL seems to be tough: though RL brokers can study to carry out very advanced duties, they don’t appear to generalize over numerous job distributions in addition to the superb generalization of supervised deep nets may lead us to count on. On this weblog submit, we’ll goal to clarify why generalization in RL is basically more durable, and certainly tougher even in principle.

We are going to present that trying to generalize in RL induces implicit partial observability, even when the RL drawback we try to unravel is a typical fully-observed MDP. This induced partial observability can considerably complicate the sorts of insurance policies wanted to generalize properly, doubtlessly requiring counterintuitive methods like information-gathering actions, recurrent non-Markovian conduct, or randomized methods. Ordinarily, this isn’t obligatory in absolutely noticed MDPs however surprisingly turns into obligatory after we contemplate generalization from a finite coaching set in a totally noticed MDP. This weblog submit will stroll via why partial observability can implicitly come up, what it means for the generalization efficiency of RL algorithms, and the way strategies can account for partial observability to generalize properly.

Studying By Instance

Earlier than formally analyzing generalization in RL, let’s start by strolling via two examples that illustrate what could make generalizing properly in RL issues tough.

The Picture Guessing Recreation: On this recreation, an RL agent is proven a picture every episode, and should guess its label as shortly as doable (Determine 1). Every timestep, the agent makes a guess; if the agent is right, then the episode ends, but when incorrect, the agent receives a adverse reward, and should make one other guess for a similar picture on the subsequent timestep. Since every picture has a singular label (that’s, there may be some “true” labelling perform $f_{true}: x mapsto y$) and the agent receives the picture as remark, it is a fully-observable RL setting.

Fig 1. The picture guessing recreation, which requires an agent to repeatedly guess labels for a picture till it will get it right. RL learns insurance policies that guess the identical label repeatedly, a technique that generalizes poorly to check photos (backside row, proper).

Suppose we had entry to an infinite variety of coaching photos, and discovered a coverage utilizing a typical RL algorithm. This coverage will study to deterministically predict the true label ($y := f_{true}(x)$), since that is the very best return technique within the MDP (as a sanity verify, recall that the optimum coverage in an MDP is deterministic and memoryless). If we solely have a restricted set of coaching photos, an RL algorithm will nonetheless study the identical technique, deterministically predicting the label it believes matches the picture. However, does this coverage generalize properly? On an unseen take a look at picture, if the agent’s predicted label is right, the very best doable reward is attained; if incorrect, the agent receives catastrophically low return, because it by no means guesses the right label. This catastrophic failure mode is ever-present, since although trendy deep nets enhance generalization and scale back the possibility of misclassification, error on the take a look at set can’t be fully decreased to 0.

Can we do higher than this deterministic prediction technique? Sure, because the discovered RL technique ignores two salient options of the guessing recreation: 1) the agent receives suggestions via an episode as as to whether its guesses are right, and a couple of) the agent can change its guess in future timesteps. One technique that higher takes benefit of those options is process-of-elimination; first, deciding on the label it considers almost certainly, and if incorrect, eliminating it and adapting to the subsequent most-likely label, and so forth. The sort of adaptive memory-based technique, nevertheless, can by no means be discovered by a typical RL algorithm like Q-learning, since they optimize MDP targets and solely study deterministic and memoryless insurance policies.

Maze-Fixing: A staple of RL generalization benchmarks, the maze-solving drawback requires an agent to navigate to a objective in a maze given a birds-eye view of the entire maze. This job is fully-observed, because the agent’s remark exhibits the entire maze. In consequence, the optimum coverage is memoryless and deterministic: taking the motion that strikes the agent alongside the shortest path to the objective. Simply as within the image-guessing recreation, by maximizing return throughout the coaching maze layouts, an RL algorithm will study insurance policies akin to this “optimum” technique – at any state, deterministically taking the motion that it considers almost certainly to be on the shortest path to the objective.

This RL coverage generalizes poorly, since if the discovered coverage ever chooses an incorrect motion, like operating right into a wall or doubling again on its outdated path, it’ll proceed to loop the identical mistake and by no means clear up the maze. This failure mode is totally avoidable, since even when the RL agent initially takes such an “incorrect” motion, after trying to comply with it, the agent receives info (e.g. the subsequent remark) as as to whether or not this was a great motion. To generalize in addition to doable, an agent ought to adapt its chosen actions if the unique actions led to surprising outcomes , however this conduct eludes normal RL targets.

Fig 2. Within the maze job, RL insurance policies generalize poorly: after they make an error, they repeatedly make the identical error, resulting in failure (left). An agent that generalizes properly should still make errors, however has the aptitude of adapting and recovering from these errors (proper). This conduct shouldn’t be discovered by normal RL targets for generalization.

What’s Going On? RL and Epistemic Uncertainty

In each the guessing recreation and the maze job, the hole between conduct discovered by normal RL algorithms and by insurance policies that really generalize properly, appeared to come up when the agent incorrectly (or couldn’t) recognized how the dynamics of the world behave. Let’s dig deeper into this phenomenon.

Fig 3. The restricted coaching dataset prevents an agent from precisely recovering the true setting. As an alternative, there may be an implicit partial observability, as an agent doesn’t know which amongst the set of “constant” environments is the true setting.

When the agent is given a small coaching set of contexts, there are a lot of dynamics fashions that match the supplied coaching contexts, however differ on held-out contexts. These conflicting hypotheses epitomize the agent’s epistemic uncertainty from the restricted coaching set. Whereas uncertainty shouldn’t be particular to RL, how it may be dealt with in RL is exclusive as a result of sequential choice making loop. For instance, the agent can actively regulate how a lot epistemic uncertainty it’s uncovered to, for instance by selecting a coverage that solely visits states the place the agent is very assured in regards to the dynamics. Much more importantly, the agent can change its epistemic uncertainty at analysis time by accounting for the data that it receives via the trajectory. Suppose for a picture within the guessing recreation, the agent is initially unsure between the t-shirt / coat labels. If the agent guesses “t-shirt” and receives suggestions that this was incorrect, the agent adjustments its uncertainty and turns into extra assured in regards to the “coat” label, which means it ought to consequently adapt and guess “coat” as an alternative.

Epistemic POMDPs and Implicit Partial Observability

Actively steering in the direction of areas of low uncertainty or taking information-gathering actions are two of a large number of avenues an RL agent has to deal with its epistemic uncertainty. Two necessary questions stay unanswered: is there a “finest” technique to deal with uncertainty? In that case, how can we describe it? From the Bayesian perspective, it seems there may be an optimum answer: generalizing optimally requires us to unravel {a partially} noticed MDP (POMDP) that’s implicitly created from the agent’s epistemic uncertainty.

This POMDP, which we name the epistemic POMDP, works as follows. Recall that as a result of the agent has solely seen a restricted coaching set, there are a lot of doable environments which are in keeping with the coaching contexts supplied. The set of constant environments will be encoded by a Bayesian posterior over environments $P(M mid D)$. Every episode within the epistemic POMDP, an agent is dropped into certainly one of these “constant” environments $M sim P(M mid D)$, and requested to maximise return inside it, however with the next necessary element: the agent shouldn’t be informed which setting $M$ it was positioned in.

This method corresponds to a POMDP (partially noticed MDP), because the related info wanted to behave is simply partially observable to the agent: though the state $s$ throughout the setting is noticed, the id of the setting $M$ that’s producing these states is hidden from the agent. The epistemic POMDP gives an instantiation of the generalization drawback into the Bayesian RL framework (see survey right here), which extra usually research optimum conduct underneath distributions over MDPs.

Fig 4. Within the epistemic POMDP, an agent interacts with a unique “constant” setting in every episode, however doesn’t know which one it’s interacting with, resulting in partial observability. To do properly, an agent should make use of a (doubtlessly memory-based) technique that works properly irrespective of which of those environments it’s positioned in.

Let’s stroll via an instance of what the epistemic POMDP appears to be like like. For the guessing recreation, the agent is unsure about precisely how photos are labelled, so every doable setting $M sim P(M mid D)$ corresponds to a unique picture labeller that’s in keeping with the coaching dataset: $f_M: X to Y$. Within the epistemic POMDP for the guessing recreation, every episode, a picture $x$ and labeller $f_M$ are chosen at random, and the agent required to output the label that’s assigned by the sampled classifier $y = f_M(x)$. The agent can not do that immediately, as a result of the id of the classifier $f_M$ is not supplied to the agent, solely the picture $x$. If all of the labellers $f_M$ within the posterior agree on the label for a sure picture, the agent can simply output this label (no partial observability). Nonetheless, if totally different classifiers assign totally different labels, the agent should use a technique that works properly on common, no matter which of the labellers was used to label the information (for instance, by adaptive process-of-elimination guessing or randomized guessing).

What makes the epistemic POMDP notably thrilling is the next equivalence:

An RL agent is Bayes-optimal for generalization if and provided that it maximizes anticipated return within the corresponding epistemic POMDP. Extra usually, the efficiency of an agent within the epistemic POMDP dictates how properly it’s anticipated to generalize at analysis time.

That generalization efficiency is dictated by efficiency within the epistemic POMDP hints at a number of classes for bridging the hole between the “optimum” technique to generalize in RL and present practices. For instance, it’s comparatively well-known that the optimum coverage in a POMDP is usually non-Markovian (adaptive primarily based on historical past), and will take information-gathering actions to scale back the diploma of partial observability. Because of this to generalize optimally, we’re more likely to want adaptive information-gathering behaviors as an alternative of the static Markovian insurance policies which are often educated.

The epistemic POMDP additionally highlights the perils of our predominant method to studying insurance policies from a restricted coaching set of contexts: operating a fully-observable RL algorithm on the coaching set. These algorithms mannequin the setting as an MDP and study MDP-optimal methods, that are deterministic and Markov. These insurance policies don’t account for partial observability, and due to this fact are inclined to generalize poorly (for instance, within the guessing recreation and maze duties). This means a mismatch between the MDP-based coaching targets which are normal in trendy algorithms and the epistemic POMDP coaching goal that really dictates how properly the discovered coverage generalizes.

Shifting Ahead with Generalization in RL

The implicit presence of partial observability at take a look at time could clarify why normal RL algorithms, which optimize fully-observed MDP targets, fail to generalize. What ought to we do as an alternative to study RL insurance policies that generalize higher? The epistemic POMDP gives a prescriptive answer: when the agent’s posterior distribution over environments will be calculated, then developing the epistemic POMDP and operating a POMDP-solving algorithm on it’ll yield insurance policies that generalize Bayes-optimally.

Sadly, in most attention-grabbing issues, this can’t be precisely carried out. Nonetheless, the epistemic POMDP can function a lodestar for designing RL algorithms that generalize higher. As a primary step, in our NeurIPS 2021 paper, we introduce an algorithm referred to as LEEP, which makes use of statistical bootstrapping to study a coverage in an approximation of the epistemic POMDP. On Procgen, a difficult generalization benchmark for RL brokers, LEEP improves considerably in test-time efficiency over PPO (Determine 3). Whereas solely a crude approximation, LEEP gives some indication that trying to study a coverage within the epistemic POMDP is usually a fruitful avenue for creating extra generalizable RL algorithms.

Fig 5. LEEP, an algorithm primarily based on the epistemic POMDP goal, generalizes higher than PPO in 4 Procgen duties.

In the event you take one lesson from this weblog submit…

In supervised studying, optimizing for efficiency on the coaching set interprets to good generalization efficiency, and it’s tempting to suppose that generalization in RL will be solved in the identical method. That is surprisingly not true; restricted coaching knowledge in RL introduces implicit partial observability into an in any other case fully-observable drawback. This implicit partial observability, as formalized by the epistemic POMDP, implies that generalizing properly in RL necessitates adaptive or stochastic behaviors, hallmarks of POMDP issues.

In the end, this highlights the incompatibility that afflicts generalization of our deep RL algorithms: with restricted coaching knowledge, our MDP-based RL targets are misaligned with the implicit POMDP goal that in the end dictates generalization efficiency.

This submit relies on the paper “Why Generalization in RL is Troublesome: Epistemic POMDPs and Implicit Partial Observability,” which is joint work with Jad Rahme (equal contribution), Aviral Kumar, Amy Zhang, Ryan P. Adams, and Sergey Levine. Because of Sergey Levine and Katie Kang for useful suggestions on the weblog submit.


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