A demonstration of the RvS policy we learn with just supervised learning and a depth-two MLP. It uses no TD learning, advantage reweighting, or Transformers!

Offline reinforcement learning (RL) is conventionally approached using value-based methods based on temporal difference (TD) learning. However, many recent algorithms reframe RL as a supervised learning problem. These algorithms learn conditional policies by conditioning on goal states (Lynch et al., 2019; Ghosh et al., 2021), reward-to-go (Kumar et al., 2019; Chen et al., 2021), or language descriptions of the task (Lynch and Sermanet, 2021).

We find the simplicity of these methods quite appealing. If supervised learning is enough to solve RL problems, then offline RL could become widely accessible and (relatively) easy to implement. Whereas TD learning must delicately balance an actor policy with an ensemble of critics, these supervised learning methods train just one (conditional) policy, and nothing else!

A demonstration of the RvS policy we learn with just supervised learning and a depth-two MLP. It uses no TD learning, advantage reweighting, or Transformers!

Offline reinforcement learning (RL) is conventionally approached using value-based methods based on temporal difference (TD) learning. However, many recent algorithms reframe RL as a supervised learning problem. These algorithms learn conditional policies by conditioning on goal states (Lynch et al., 2019; Ghosh et al., 2021), reward-to-go (Kumar et al., 2019; Chen et al., 2021), or language descriptions of the task (Lynch and Sermanet, 2021).

We find the simplicity of these methods quite appealing. If supervised learning is enough to solve RL problems, then offline RL could become widely accessible and (relatively) easy to implement. Whereas TD learning must delicately balance an actor policy with an ensemble of critics, these supervised learning methods train just one (conditional) policy, and nothing else!

Figure 1: Airmass measurements over Ukraine from February 18, 2022 – March 01, 2022 from the SEVIRI instrument. Data accessed via the EUMETSAT Viewer.

Satellite imagery is a critical source of information during the current invasion of Ukraine. Military strategists, journalists, and researchers use this imagery to make decisions, unveil violations of international agreements, and inform the public of the stark realities of war. With Ukraine experiencing a large amount of cloud cover and attacks often occuring during night-time, many forms of satellite imagery are hindered from seeing the ground. Synthetic aperture radar imagery penetrates cloud cover, but requires special training to interpret. Automating this tedious task would enable real-time insights, but current computer vision methods developed on typical RGB imagery do not properly account for the phenomenology of SAR. This leads to suboptimal performance on this critical modality. Improving the access to and availability of SAR-specific methods, codebases, datasets, and pretrained models will benefit intelligence agencies, researchers, and journalists alike during this critical time for Ukraine.

In this post, we present a baseline method and pretrained models that enable the interchangeable use of RGB and SAR for downstream classification, semantic segmentation, and change detection pipelines.

Figure 1: Airmass measurements (clouds) over Ukraine from February 18, 2022 – March 01, 2022 from the SEVIRI instrument. Data accessed via the EUMETSAT Viewer.

Satellite imagery is a critical source of information during the current invasion of Ukraine. Military strategists, journalists, and researchers use this imagery to make decisions, unveil violations of international agreements, and inform the public of the stark realities of war. With Ukraine experiencing a large amount of cloud cover and attacks often occuring during night-time, many forms of satellite imagery are hindered from seeing the ground. Synthetic Aperture Radar (SAR) imagery penetrates cloud cover, but requires special training to interpret. Automating this tedious task would enable real-time insights, but current computer vision methods developed on typical RGB imagery do not properly account for the phenomenology of SAR. This leads to suboptimal performance on this critical modality. Improving the access to and availability of SAR-specific methods, codebases, datasets, and pretrained models will benefit intelligence agencies, researchers, and journalists alike during this critical time for Ukraine.

In this post, we present a baseline method and pretrained models that enable the interchangeable use of RGB and SAR for downstream classification, semantic segmentation, and change detection pipelines.

Unsupervised Reinforcement Learning (RL), where RL agents pre-train with self-supervised rewards, is an emerging paradigm for developing RL agents that are capable of generalization. Recently, we released the Unsupervised RL Benchmark (URLB) which we covered in a previous post. URLB benchmarked many unsupervised RL algorithms across three categories — competence-based, knowledge-based, and data-based algorithms. A surprising finding was that competence-based algorithms significantly underperformed other categories. In this post we will demystify what has been holding back competence-based methods and introduce Contrastive Intrinsic Control (CIC), a new competence-based algorithm that is the first to achieve leading results on URLB.

imodels: A python package with cutting-edge techniques for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use.

Recent machine-learning advances have led to increasingly complex predictive models, often at the cost of interpretability. We often need interpretability, particularly in high-stakes applications such as medicine, biology, and political science (see here and here for an overview). Moreover, interpretable models help with all kinds of things, such as identifying errors, leveraging domain knowledge, and speeding up inference.

Despite new advances in formulating/fitting interpretable models, implementations are often difficult to find, use, and compare. imodels (github, paper) fills this gap by providing a simple unified interface and implementation for many state-of-the-art interpretable modeling techniques, particularly rule-based methods.

The shortcomings of supervised RL

Reinforcement Learning (RL) is a powerful paradigm for solving many problems of interest in AI, such as controlling autonomous vehicles, digital assistants, and resource allocation to name a few. We’ve seen over the last five years that, when provided with an extrinsic reward function, RL agents can master very complex tasks like playing Go, Starcraft, and dextrous robotic manipulation. While large-scale RL agents can achieve stunning results, even the best RL agents today are narrow. Most RL algorithms today can only solve the single task they were trained on and do not exhibit cross-task or cross-domain generalization capabilities.

A side-effect of the narrowness of today’s RL systems is that today’s RL agents are also very data inefficient. If we were to train AlphaGo-like agents on many tasks each agent would likely require billions of training steps because today’s RL agents don’t have the capabilities to reuse prior knowledge to solve new tasks more efficiently. RL as we know it is supervised – agents overfit to a specific extrinsic reward which limits their ability to generalize.

Processing raw sensory inputs is crucial for applying deep RL algorithms to real-world problems.
For example, autonomous vehicles must make decisions about how to drive safely given information flowing from cameras, radar, and microphones about the conditions of the road, traffic signals, and other cars and pedestrians.
However, direct “end-to-end” RL that maps sensor data to actions (Figure 1, left) can be very difficult because the inputs are high-dimensional, noisy, and contain redundant information.
Instead, the challenge is often broken down into two problems (Figure 1, right): (1) extract a representation of the sensory inputs that retains only the relevant information, and (2) perform RL with these representations of the inputs as the system state.

Figure 1. Representation learning can extract compact representations of states for RL.

A wide variety of algorithms have been proposed to learn lossy state representations in an unsupervised fashion (see this recent tutorial for an overview).
Recently, contrastive learning methods have proven effective on RL benchmarks such as Atari and DMControl (Oord et al. 2018, Stooke et al. 2020, Schwarzer et al. 2021), as well as for real-world robotic learning (Zhan et al.).
While we could ask which objectives are better in which circumstances, there is an even more basic question at hand: are the representations learned via these methods guaranteed to be sufficient for control?
In other words, do they suffice to learn the optimal policy, or might they discard some important information, making it impossible to solve the control problem?
For example, in the self-driving car scenario, if the representation discards the state of stoplights, the vehicle would be unable to drive safely.
Surprisingly, we find that some widely used objectives are not sufficient, and in fact do discard information that may be needed for downstream tasks.

Long-horizon predictions of (top) the Trajectory Transformer compared to those of (bottom) a single-step dynamics model.

Modern machine learning success stories often have one thing in common: they use methods that scale gracefully with ever-increasing amounts of data.
This is particularly clear from recent advances in sequence modeling, where simply increasing the size of a stable architecture and its training set leads to qualitatively different capabilities.¹

Meanwhile, the situation in reinforcement learning has proven more complicated.
While it has been possible to apply reinforcement learning algorithms to large–scale problems, generally there has been much more friction in doing so.
In this post, we explore whether we can alleviate these difficulties by tackling the reinforcement learning problem with the toolbox of sequence modeling.
The end result is a generative model of trajectories that looks like a large language model and a planning algorithm that looks like beam search.
Code for the approach can be found here.

Fig. 1: The BRIDGE dataset contains 7200 demonstrations of kitchen-themed manipulation tasks across 71 tasks in 10 domains. Note that any GIF compression artifacts in this animation are not present in the dataset itself.

When we apply robot learning methods to real-world systems, we must usually collect new datasets for every task, every robot, and every environment. This is not only costly and time-consuming, but it also limits the size of the datasets that we can use, and this, in turn, limits generalization: if we train a robot to clean one plate in one kitchen, it is unlikely to succeed at cleaning any plate in any kitchen. In other fields, such as computer vision (e.g., ImageNet) and natural language processing (e.g., BERT), the standard approach to generalization is to utilize large, diverse datasets, which are collected once and then reused repeatedly. Since the dataset is reused for many models, tasks, and domains, the up-front cost of collecting such large reusable datasets is worth the benefits. Thus, to obtain truly generalizable robotic behaviors, we may need large and diverse datasets, and the only way to make this practical is to reuse data across many different tasks, environments, and labs (i.e. different background lighting conditions, etc.).