Reducing the Computational Cost of Deep Reinforcement Learning Research
It is widely accepted that the enormous growth of deep reinforcement learning research, which combines traditional reinforcement learning with deep neural networks, began with the publication of the seminal DQN algorithm. This paper demonstrated the potential of this combination, showing that it could produce agents that could play a number of Atari 2600 games very effectively. Since then, there have been several approaches that have built on and improved the original DQN. The popular Rainbow algorithm combined a number of these recent advances to achieve state-of-the-art performance on the ALE benchmark. This advance, however, came at a very high computational cost, which has the unfortunate side effect of widening the gap between those with ample access to computational resources and those without.
In “Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research”, to be presented at ICML 2021, we revisit this algorithm on a set of small- and medium-sized tasks. We first discuss the computational cost associated with the Rainbow algorithm. We explore how the same conclusions regarding the benefits of combining the various algorithmic components can be reached with smaller-scale experiments, and further generalize that idea to how research done on a smaller computational budget can provide valuable scientific insights.
The Cost of Rainbow
A major reason for the computational cost of Rainbow is that the standards in academic publishing often require evaluating new algorithms on large benchmarks like ALE, which consists of 57 Atari 2600 games that reinforcement learning agents may learn to play. For a typical game, it takes roughly five days to train a model using a Tesla P100 GPU. Furthermore, if one wants to establish meaningful confidence bounds, it is common to perform at least five independent runs. Thus, to train Rainbow on the full suite of 57 games required around 34,200 GPU hours (or 1425 days) in order to provide convincing empirical performance statistics. In other words, such experiments are only feasible if one is able to train on multiple GPUs in parallel, which can be prohibitive for smaller research groups.
As in the original Rainbow paper, we evaluate the effect of adding the following components to the original DQN algorithm: double Q-learning, prioritized experience replay, dueling networks, multi-step learning, distributional RL, and noisy nets.
We evaluate on a set of four classic control environments, which can be fully trained in 10-20 minutes (compared to five days for ALE games):
Upper left: In CartPole, the task is to balance a pole on a cart that the agent can move left and right. Upper right: In Acrobot, there are two arms and two joints, where the agent applies force to the joint between the two arms in order to raise the lower arm above a threshold. Lower left: In LunarLander, the agent is meant to land the spaceship between the two flags. Lower right: In MountainCar, the agent must build up momentum between two hills to drive to the top of the rightmost hill.
We investigated the effect of both independently adding each of the components to DQN, as well as removing each from the full Rainbow algorithm. As in the original Rainbow paper, we find that, in aggregate, the addition of each of these algorithms does improve learning over the base DQN. However, we also found some important differences, such as the fact that distributional RL — commonly thought to be a positive addition on its own — does not always yield improvements on its own. Indeed, in contrast to the ALE results in the Rainbow paper, in the classic control environments, distributional RL only yields an improvement when combined with another component.
Each plot shows the training progress when adding the various components to DQN. The x-axis is training steps,the y-axis is performance (higher is better). Each plot shows the training progress when removing the various components from Rainbow. The x-axis is training steps,the y-axis is performance (higher is better).
We also re-ran the Rainbow experiments on the MinAtar environment, which consists of a set of five miniaturized Atari games, and found qualitatively similar results. The MinAtar games are roughly 10 times faster to train than the regular Atari 2600 games on which the original Rainbow algorithm was evaluated, but still share some interesting aspects, such as game dynamics and having pixel-based inputs to the agent. As such, they provide a challenging mid-level environment, in between the classic control and the full Atari 2600 games.
When viewed in aggregate, we find our results to be consistent with those of the original Rainbow paper — the impact resulting from each algorithmic component can vary from environment to environment. If we were to suggest a single agent that balances the tradeoffs of the different algorithmic components, our version of Rainbow would likely be consistent with the original, in that combining all components produces a better overall agent. However, there are important details in the variations of the different algorithmic components that merit a more thorough investigation.
Beyond the Rainbow
When DQN was introduced, it made use of the Huber loss and the RMSProp Optimizer. It has been common practice for researchers to use these same choices when building on DQN, as most of their effort is spent on other algorithmic design decisions. In the spirit of reassessing these assumptions, we revisited the loss function and optimizer used by DQN on a lower-cost, small-scale classic control and MinAtar environments. We ran some initial experiments using the Adam optimizer, which has lately been the most popular optimizer choice, combined with a simpler loss function, the mean-squared error loss (MSE). Since the selection of optimizer and loss function is often overlooked when developing a new algorithm, we were surprised to see that we observed a dramatic improvement on all the classic control and MinAtar environments.
We thus decided to evaluate the different ways of combining the two optimizers (RMSProp and Adam) with the two losses (Huber and MSE) on the full ALE suite (60 Atari 2600 games). We found that Adam+MSE is a superior combination than RMSProp+Huber.
Additionally, when comparing the various optimizer-loss combinations, we find that when using RMSProp, the Huber loss tends to perform better than MSE (illustrated by the gap between the solid and dotted orange lines).
On a limited computational budget we were able to reproduce, at a high-level, the findings of the Rainbow paper and uncover new and interesting phenomena. Evidently it is much easier to revisit something than to discover it in the first place. Our intent with this work, however, was to argue for the relevance and significance of empirical research on small- and medium-scale environments. We believe that these less computationally intensive environments lend themselves well to a more critical and thorough analysis of the performance, behaviors, and intricacies of new algorithms.
We are by no means calling for less emphasis to be placed on large-scale benchmarks. We are simply urging researchers to consider smaller-scale environments as a valuable tool in their investigations, and reviewers to avoid dismissing empirical work that focuses on smaller-scale environments. By doing so, in addition to reducing the environmental impact of our experiments, we will get both a clearer picture of the research landscape and reduce the barriers for researchers from diverse and often underresourced communities, which can only help make our community and scientific advances stronger.
Thank you to Johan, the first author of this paper, for his hard work and persistence in seeing this through! We would also like to thank Marlos C. Machado, Sara Hooker, Matthieu Geist, Nino Vieillard, Hado van Hasselt, Eleni Triantafillou, and Brian Tanner for their insightful comments on this work.