Can a deep learning classifier, trained on a dataset derived from a reinforcement learning (RL) agent's interactions with an environment, achieve the same performance as the RL agent itself? Assuming an environment that can generate unlimited data, where the RL agent's actions and outcomes are recorded and formatted as a classification dataset (with states as inputs and actions as labels, assume you have the same allocation strategy of final reward to each earlier actions), would there be a mathematical equivalence between the RL approach and the deep learning classifier's approach? If there's a performance discrepancy, what additional information or capabilities does RL possess that a deep learning classifier does not?
4
-
1$\begingroup$ I don’t even see these as solving the same problem. Deep learning makes a prediction. Reinforcement learning suggests an action. $\endgroup$– DaveCommented Mar 3 at 6:16
-
$\begingroup$ Connection is there but in a slightly different setting, this is how DQN works actually, i.e., deep Q-network (DQN). $\endgroup$– patagonicusCommented Mar 3 at 6:28
-
$\begingroup$ If you frame the problem in different ways, they could solve the same problem. Like the atari games, if you frame the problem as given state, classify the best action, then it is a classification problem. Your classifier could be trained on the same data of an agent playing the game 1 million times. Just wondering if RL can see anything more information or have some structural benefit. $\endgroup$– yangCommented Mar 3 at 16:53
-
$\begingroup$ @yang What do you mean by classify the “best” action? An action is the “best” when that action, under a particular set of circumstances, leads to an optimal outcome. $\endgroup$– DaveCommented Mar 3 at 18:12
Add a comment
|
3 Answers
$\begingroup$
$\endgroup$
4
You are comparing Reinforcement Learning and Supervised Learning (what you call Deep Learning Classifier), these are different paradigms, different inputs, and more importantly, different kind of supervision.
Reinforcement Learning uses a reward signal as supervision, there is no "correct answer" here.
Supervised Learning uses labels as supervision that provide a correct answer.
The two are not comparable, you can use neural networks inside reinforcement learning algorithms (like DQN).
-
$\begingroup$ the reward signal can be treated as labels right? They carry the same information from the environment aren't they? $\endgroup$– yangCommented Mar 3 at 16:47
-
$\begingroup$ @yang No, I explicitly said that in my answer, reward does not tell you what is the "right answer". $\endgroup$ Commented Mar 3 at 16:53
-
$\begingroup$ I guess the model parameters can't distinguish between the 'right answer' and a gradient update. They are getting values, no matter what you call those values right? $\endgroup$– yangCommented Mar 3 at 16:58
-
$\begingroup$ @yang Its not the name of the values, its what information they contain, in SL labels are the right answer, in RL rewards give you a vague idea of performance metric, but not the right action at each timestep. $\endgroup$ Commented Mar 3 at 17:01
$\begingroup$
$\endgroup$
Indeed as you rightly conceived that the problem of learning a policy $\pi$ to choose actions is similar in some respects to the function approximation problems usually dealt with supervised learning. In RL the input is a (current) state $s$ in the state space and the output is an action $a$ in the action space. Thus in order to put RL into SL framework each training example would be a pair of the form $(s, \pi(s))$ as you also suggested. However, RL differs from SL in several important respects:
In RL training data is not available in this form, instead the environment only provides a sequence of immediate rewards as the agent executes its sequence of actions. Therefore it faces the problem of temporal credit assignment determining which of the actions in its sequence are to be credited with producing the eventual rewards. This cannot be fully captured by a supervised classification model which typically treats each training example independently.
By above RL's temporally dependent nature, the agent influences the distribution of training examples fed to itself by the action sequence it chooses, thus the learner faces a tradeoff in choosing whether to favor exploration of unknown states and actions or exploitation of states and actions that it has already learned yielding high cumulative reward. SL learner doesn't have this self-imposed problem at all and if it just chooses exploitation based on current cumulative reward, then it's just similar to overfitting and won't generalize well to unseen situations.
RL agents often work with a state space that might be partially observable or have a high-dimensional representation. If the RL agent relies on features or patterns in the state that are not easily captured by the supervised classifier architecture, the classifier might struggle to achieve the same level of performance. For instance a robot only can see what's before its camera at any time step, your SL feature is thus very limited and rigid, while RL can also flexibly use its previous observations and learn a policy to improve its observability via changing angles.
Having said that, some imitation learning such as behavior cloning as a branch of RL does use SL framework as referenced here, where the input feature is the learned policy and the target is expert demonstrations.
RL also suffer from several drawbacks. First, determining an appropriate reward function that can accurately represent the true performance objectives can be challenging. Second, rewards may be sparse, which makes the learning process expensive in terms of both the required amount of data and in the number of failures that may be experienced when exploring with a suboptimal policy... This chapter will first introduce two classical approaches to imitation learning (behavior cloning and the DAgger algorithm) that focus on directly imitating the policy... Behavior cloning approaches use a set of expert demonstrations... This can be accomplished through supervised learning techniques, where the difference between the learned policy and expert demonstrations are minimized with respect to some metric
$\begingroup$
$\endgroup$
Reinforcement learning totally depends on four parameters (agent, environment, reward, and actions). Suppose we get the data with states as inputs and actions as labels. and we apply deep learning classifier to classify each action Sometimes, there might be a situation where agents work on states that cannot be classified using a deep learning-based approach. RL involves exploration to discover optimal policies. A classifier may not have the ability to explore new strategies beyond what is present in the training dataset. So whatever you said is possible, but there are several important considerations and potential challenges.
