Determining what experience to generate to best facilitate learning (i.e. exploration) is one of the distinguishing features and open challenges in reinforcement learning. The advent of distributed agents that interact with parallel instances of the environment has enabled larger scale and greater flexibility, but has not removed the need to tune or tailor exploration to the task, because the ideal data for the learning algorithm necessarily depends on its process of learning. We propose to dynamically adapt the data generation by using a non-stationary multi-armed bandit to optimize a proxy of the learning progress. The data distribution is controlled via modulating multiple parameters of the policy (such as stochasticity, consistency or optimism) without significant overhead. The adaptation speed of the bandit can be increased by exploiting the factored modulation structure. We demonstrate on a suite of Atari 2600 games how this unified approach produces results comparable to per-task tuning at a fraction of the cost.
This papers studies how to explore, in order to generate experience for faster learning of policies in context of RL. RL methods typically employ simple hand-tuned exploration schedules (such as epsilon greedy exploration, and changing the epsilon as training proceeds). This paper proposes a scheme for learning this schedule. The paper does this by modeling this as a non-stationary multi-arm bandit problem. Different exploration settings (tuple of choice of exploration, and the exact hyper-parameter), are considered as different non-stationary multi-arm bandits (while also employing some factorization) and expected returns are maintained over training. Arm (exploration strategy and hyper-parameter) is picked according to the return. The paper demonstrates results on the Atari suite of RL benchmarks, and shows results that demonstrate that their proposed search leads to faster learning.
Strength:
1. The paper tackles an interesting and important problem. The proposed solution is simple, yet effective.
Shortcomings:
1. The presentation is somewhat convoluted. The paper motivates the problem that we need to pick out an exploration sequence that optimizes learning progress, but then approximates it as simply measuring the return. Given there is no theoretical justification for the approximation, I believe the paper claims more than what it delivers and should change the presentation, so as not to claim that it is measuring and capturing learning progress to learn faster.
2. I am confused by Figure 4, and in general with the relative rank metrics. Specifically, in Figure 4, is it that the proposed bandit approach not as good as picking a single hyper-parameter for the different settings (T=0.01, eps=0.01, omega=2.0)? Similarly, for Figure 2, a singe fixed z, seems to do better than the bandit versions. Why doesn't the proposed bandit algorithm not pick out the best hyper-parameter? How well would a simpler hyper-parameter search procedure (picking the best hyper-parameter after the first 2000 episodes)?
3. This apart, I think that the experiment section is pretty hard to read, given all the metrics and methodology is in the Appendix. An alternate organization that presents all the main results in the main body in a self-contained manner will help.
4. Comparison with past works. I believe there are other existing works that should be cited and compared to. Using bandits to decide between different hyper-parameters is common (for example, see [A] for a service to do this with ML models), [B] uses improvements in accuracy as a way to pick between which question type to train on. Such past works should be cited and compared against.
[A] https://ai.google/research/pubs/pub46180
[B] Learning by Asking Questions
Ishan Misra, Ross Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta and Laurens van der Maaten