Reinforcement learning has seen a great deal of success in solving complex decision making problems ranging from robotics to games to supply chain management to recommender systems. Despite their success, deep reinforcement learning algorithms can be exceptionally difficult to use, due to unstable training, sensitivity to hyperparameters, and generally unpredictable and poorly understood convergence properties. Multiple explanations, and corresponding solutions, have been proposed for improving the stability of such methods, and we have seen good progress over the last few years on these algorithms. In this blog post, we will dive deep into analyzing a central and underexplored reason behind some of the problems with the class of deep RL algorithms based on dynamic programming, which encompass the popular DQN and soft actor-critic (SAC) algorithms – the detrimental connection between data distributions and learned models.
Before diving deep into a description of this problem, let us quickly recap
some of the main concepts in dynamic programming. Algorithms that apply dynamic
programming in conjunction with function approximation are generally referred
to as approximate dynamic programming (ADP) methods. ADP algorithms include
some of the most popular, state-of-the-art RL methods such as variants of deep
Q-networks (DQN) and soft actor-critic (SAC) algorithms. ADP methods based on
Q-learning train action-value functions, , via a Bellman backup. In
practice, this corresponds to training a parametric function, , by minimizing the mean squared difference to a backup estimate of the
Q-function, defined as:
where denotes a previous instance of the original Q-function,
, and is commonly referred to as a target network. This update is
summarized in the equation below.
An analogous update is also used for
methods that also maintain an explicitly parametrized policy,
, alongside a Q-function. Such an update typically replaces
with an expectation under the policy, . We shall use the version for consistency throughout,
however, the actor-critic version follows analogously. These ADP methods aim at
learning the optimal value function, , by applying the Bellman backup
iteratively untill convergence.
A central factor that affects the performance of ADP algorithms is the choice
of the training data-distribution, , as shown in the equation
above. The choice of is an integral component of the backup,
and it affects solutions obtained via ADP methods, especially since function
approximation is involved. Unlike tabular settings, function approximation
causes the learned Q function to depend on the choice of data distribution
, thereby affecting the dynamics of the learning process. We
show that on-policy exploration induces distributions such that
training Q-functions under may fail to correct systematic
errors in the Q-function, even if Bellman error is minimized as much as
possible – a phenomenon that we refer to as an absence of corrective
Corrective Feedback and Why it is Absent in ADP
What is corrective feedback formally? How do we determine if it is present or
absent in ADP methods? In order to build intuition, we first present a simple
contextual bandit (one step RL) example, where the Q-function is trained to
match via supervised updates, without bootstrapping. This enjoys
corrective feedback, and we then contrast it with ADP methods, which do not. In
this example, the goal is to learn the optimal value function ,
which, is equal to the reward . At iteration , the algorithm
minimizes the estimation error of the Q-function:
Using an -greedy or Boltzmann policy for exploration, denoted by $pi_k$, gives rise
to a hard negative mining phenomenon – the policy chooses precisely those
actions that correspond to possibly over-estimated Q-values for each state
and observes the corresponding, or , as a
result. Then, minimizing , on samples collected this way
corrects errors in the Q-function, as is pushed closer to match
for actions with incorrectly high Q-values, correcting
precisely the Q-values which may cause sub-optimal performance. This
constructive interaction between online data collection and error correction –
where the induced online data distribution corrects errors in the value
function – is what we refer to as corrective feedback.
In contrast, we will demonstrate that ADP methods that rely on previous
Q-functions to generate targets for training the current Q-function, may not
benefit from corrective feedback. This difference between bandits and ADP
happens because the target values are computed by applying a Bellman backup on
the previous Q-function, (target value), rather than the optimal
, so, errors in , at the next states can result in incorrect
Q-value targets at the current state. No matter how often the current
transition is observed, or how accurately Bellman errors are minimized, the
error in the Q-value with respect to the optimal Q-function, , at
this state is not reduced. Furthermore, in order to obtain correct target
values, we need to ensure that values at state-action pairs occurring at the
tail ends of the data distribution , which are primary causes of
errors in Q-values at other states, are correct. However, as we will show via a
simple didactic example, that this correction process may be extremely slow and
may not occur, mainly because of undesirable generalization effects of the
Let’s consider a didactic example of a tree-structured deterministic MDP with 7
states and 2 actions, and , at each state.
Figure 1: Run of an ADP algorithm with on-policy data collection. Boxed nodes
and circled nodes denote groups of states aliased by function approximation —
values of these nodes are affected due to parameter sharing and function
A run of an ADP algorithm that chooses the current on-policy state-action
marginal as on this tree MDP is shown in Figure 1. Thus,
the Bellman error at a state is minimized in proportion to the frequency of
occurrence of that state in the policy state-action marginal. Since the leaf
node states are the least frequent in this on-policy marginal distribution (due
to the discounting), the Bellman backup is unable to correct errors in Q-values
at such leaf nodes, due to their low frequency and aliasing with other states
arising due to function approximation. Using incorrect Q-values at the leaf
nodes to generate targets for other nodes in the tree, just gives rise to
incorrect values, even if Bellman error is fully minimized at those states.
Thus, most of the Bellman updates do not actually bring Q-values at the states
of the MDP closer to , since the primary cause of incorrect target
values isn’t corrected.
This observation is surprising, since it demonstrates how the choice of an
online distribution coupled with function approximation might actually learn
incorrect Q-values. On the other hand, a scheme that chooses to update states
level by level progressively (Figure 2), ensuring that target values used at
any iteration of learning are correct, very easily learns correct Q-values in
Figure 2: Run of an ADP algorithm with an oracle distribution, that updates
states level-by level, progressing through the tree from the leaves to the
root. Even in the presence of function approximation, selecting the right set
of nodes for updates gives rise to correct Q-values.
Consequences of Absent Corrective Feedback
Now, one might ask if an absence of corrective feedback occurs in practice,
beyond a simple didactic example and whether it hurts in practical problems.
Since visualizing the dynamics of the learning process is hard in practical
problems as we did for the didactic example, we instead devise a metric that
quantifies our intuition for corrective feedback. This metric, what we call
value error, is given by:
Increasing values of imply that the algorithm is pushing
Q-values farther away from , which means that corrective feedback is
absent, if this happens over a number of iterations. On the other hand,
decreasing values of implies that the algorithm is
continuously improving its estimate of , by moving it towards with
each iteration, indicating the presence of corrective feedback.
Observe in Figure 3, that ADP methods can suffer from prolonged periods where
this global measure of error in the Q-function, , is
increasing or fluctuating, and the corresponding returns degrade or stagnate,
implying an absence of corrective feedback.
Figure 3: Consequences of absent corrective feedback, including (a) sub-optimal
convergence, (b) instability in learning and (c) inability to learn with sparse
In particular, we describe three different consequences of an absence of
Convergence to suboptimal Q-functions. We find that on-policy sampling
can cause ADP to converge to a suboptimal solution, even in the absence of
sampling error. Figure 3(a) shows that the value error
rapidly decreases initially, and eventually converges to a value significantly
greater than 0, from which the learning process never recovers.
Instability in the learning process. We observe that ADP with replay
buffers can be unstable. For instance, the algorithm is prone to degradation
even if the latest policy obtains returns that are very close to the optimal
return in Figure 3(b).
Inability to learn with low signal-to-noise ratio. Absence of corrective
feedback can also prevent ADP algorithms from learning quickly in scenarios
with low signal-to-noise ratio, such as tasks with sparse/noisy rewards as
shown in Figure 3(c). Note that this is not an exploration issue, since all
transitions in the MDP are provided to the algorithm in this experiment.
Inducing Maximal Corrective Feedback via Distribution Correction
Now that we have defined corrective feedback and gone over some detrimental
consequences an absence of it can have on the learning process of an ADP
algorithm, what might be some ways to fix this problem? To recap, an absence of
corrective feedback occurs when ADP algorithms naively use the on-policy or
replay buffer distributions for training Q-functions. One way to prevent this
problem is by computing an “optimal” data distribution that provides maximal
corrective feedback, and train Q-functions using this distribution? This way we
can ensure that the ADP algorithm always enjoys corrective feedback, and hence
makes steady learning progress. The strategy we used in our work is to compute
this optimal distribution and then perform a weighted Bellman update that
re-weights the data distribution in the replay buffer to this optimal
distribution (in practice, a tractable approximation is required, as we will
see) via importance sampling based techniques.
We will not go into the full details of our derivation in this article,
however, we mention the optimization problem used to obtain a form for this
optimal distribution and encourage readers interested in the theory to checkout
Section 4 in our paper. In this optimization problem, our goal is to minimize
a measure of corrective feedback, given by value error ,
with respect to the distribution used for Bellman error minimization,
at every iteration . This gives rise to the following problem:
We show in our paper that the solution of this optimization problem, that we
refer to as the optimal distribution, , is given by:
By simplifying this expression, we obtain a practically viable expression for
weights, , at any iteration that can be used to re-weight the data
where is the accumulated Bellman error over iterations, and it
satisfies a convenient recursion making it amenable to practical
and is the Boltzmann or greedy policy
corresponding to the current Q-function.
What does this expression for intuitively correspond to? Observe that
the term appearing in the exponent in the expression for corresponds to
the accumulated Bellman error in the target values. Our choice of ,
thus, basically down-weights transitions with highly incorrect target values.
This technique falls into a broader class of abstention based techniques
that are common in supervised learning settings with noisy labels, where
down-weighting datapoints (transitions here) with errorful labels (target
values here) can boost generalization and correctness properties of the learned
Figure 4: Schematic of the DisCor algorithm. Transitions with errorful target
values are downweighted.
Why does our choice of , i.e. the sum of accumulated Bellman errors
suffice? This is because this value accounts for how error is
propagated in ADP methods. Bellman errors, are
propagated under the current policy , and then discounted when
computing target values for updates in ADP. captures exactly this,
and therefore, using this estimate in our weights suffices.
Our practical algorithm, that we refer to as DisCor (Distribution
Correction), is identical to conventional ADP methods like Q-learning, with the
exception that it performs a weighted Bellman backup – it assigns a weight
to a transition, and performs a Bellman backup
weighted by these weights, as shown below.
We depict the general principle in the schematic diagram shown in Figure 4.
How does DisCor perform in practice?
We finally present some results that demonstrate the efficacy of our method,
DisCor, in practical scenarios. Since DisCor only modifies the chosen
distribution for the Bellman update, it can be applied on top of any standard
ADP algorithm including soft actor-critic (SAC) or deep Q-network (DQN). Our
paper presents results for a number of tasks spanning a wide variety of
settings including robotic manipulation tasks, multi-task reinforcement
learning tasks, learning with stochastic and noisy rewards, and Atari games.
In this blog post, we present two of these results from robotic manipulation
and multi-task RL.
Robotic manipulation tasks. On six challenging benchmark tasks from the
MetaWorld suite, we observe that DisCor
when combined with SAC greatly outperforms prior state-of-the-art RL
algorithms such as soft actor-critic (SAC) and prioritized experience replay
(PER) which is a prior method that
prioritizes states with high Bellman error during training. Note that DisCor
usually starts learning earlier than other methods compared to. DisCor
outperforms vanilla SAC by a factor of about 50% on average, in terms of
success rate on these tasks.
Multi-task reinforcement learning. We also present certain results on
the Multi-task 10 (MT10) and Multi-task 50 (MT50) benchmarks from the
Meta-world suite. The goal here is to learn a single policy that can solve a
number of (10 or 50, respectively) different manipulation tasks that share
common structure. We note that DisCor outperforms, state-of-the-art SAC
algorithm on both of these benchmarks by a wide margin (for e.g. 50% on
MT10, success rate). Unlike the learning process of SAC that tends to
plateau over the course of learning, we observe that DisCor always exhibits
a non-zero gradient for the learning process, until it converges.
In our paper, we also perform evaluations on other domains such as Atari games
and OpenAI gym benchmarks, and we encourage the readers to check those out. We
also perform an analysis of the method on tabular domains, understanding
different aspects of the method.
Perspectives, Future Work and Open Problems
Some of our and
other prior work has highlighted the impact
of the data distribution on the performance of ADP algorithms, We observed in
another prior work that in contrast to the
intuitive belief about the efficacy of online Q-learning with on-policy data
collection, Q-learning with a uniform distribution over states and actions
seemed to perform best. Obtaining a uniform distribution over state-action
tuples during training is not possible in RL, unless all states and actions are
observed at least once, which may not be the case in a number of scenarios. We
might also ask the question about whether the uniform distribution is the best
choice that can be used in an RL setting? The form of the optimal distribution
derived in Section 4 of our paper, is a potentially better choice since it is
customized to the MDP under consideration.
Furthermore, in the domain of purely offline reinforcement learning, studied in
our prior work and some other works, such
as this and
this, we observe that the data distribution
is again a central feature, where backing up out-of-distribution actions and
the inability to try these actions out in the environment to obtain answers to
counterfactual queries, can cause error accumulation and backups to diverge.
However, in this work, we demonstrate a somewhat counterintuitive finding: even
with on-policy data collection, where the algorithm, in principle, can evaluate
all forms of counterfactual queries, the algorithm may not obtain a steady
learning progress, due to an undesirable interaction between the data
distribution and generalization effects of the function approximator.
What might be a few promising directions to pursue in future work?
Formal analysis of learning dynamics: While our study is an initial foray
into the role that data distributions play in the learning dynamics of ADP
algorithms, this motivates a significantly deeper direction of future study. We
need to answer questions related to how deep neural network based function
approximators actually behave, which are behind these ADP methods, in order to
get them to enjoy corrective feedback.
Re-weighting to supplement exploration in RL problems: Our work depicts the
promise of re-weighting techniques as a practically simple replacement for
altering entire exploration strategies. We believe that re-weighting techniques
are very promising as a general tool in our toolkit to develop RL algorithms.
In an online RL setting, re-weighting can help remove the some of the burden
off exploration algorithms, and can thus, potentially help us employ complex
exploration strategies in RL algorithms.
More generally, we would like to make a case of analyzing effects of data
distribution more deeply in the context of deep RL algorithms. It is well known
that narrow distributions can lead to brittle solutions in supervised learning
that also do not generalize. What is the corresponding analogue in
reinforcement learning? Distributional robustness style techniques have been
used in supervised learning to guarantee a uniformly convergent learning
process, but it still remains unclear how to apply these in an RL with function
approximation setting. Part of the reason is that the theory of RL often
derives from tabular settings, where distributions do not hamper the learning
process to the extent they do with function approximation. However, as we
showed in this work, choosing the right distribution may lead to significant
gains in deep RL methods, and therefore, we believe, that this issue should be
studied in more detail.
This blog post is based on our recent paper:
- DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction
Aviral Kumar, Abhishek Gupta, Sergey Levine
We thank Sergey Levine and Marvin Zhang for their valuable feedback on this blog post.