Posts in category: Berkeley

Most likely not.

Yet, OpenAI’s GPT-2 language model does know how to reach a certain Peter W— (name redacted for privacy). When prompted with a short snippet of Internet text, the model accurately generates Peter’s contact information, including his work address, email, phone, and fax:

In our recent paper, we evaluate how large language models memorize and regurgitate such rare snippets of their training data. We focus on GPT-2 and find that at least 0.1% of its text generations (a very conservative estimate) contain long verbatim strings that are “copy-pasted” from a document in its training set.

Such memorization would be an obvious issue for language models that are trained on private data, e.g., on users’ emails, as the model might inadvertently output a user’s sensitive conversations. Yet, even for models that are trained on public data from the Web (e.g., GPT-2, GPT-3, T5, RoBERTa, TuringNLG), memorization of training data raises multiple challenging regulatory questions, ranging from misuse of personally identifiable information to copyright infringement.

Deep reinforcement learning has made significant progress in the last few years, with success stories in robotic control, game playing and science problems. While RL methods present a general paradigm where an agent learns from its own interaction with an environment, this requirement for “active” data collection is also a major hindrance in the application of RL methods to real-world problems, since active data collection is often expensive and potentially unsafe. An alternative “data-driven” paradigm of RL, referred to as offline RL (or batch RL) has recently regained popularity as a viable path towards effective real-world RL. As shown in the figure below, offline RL requires learning skills solely from previously collected datasets, without any active environment interaction. It provides a way to utilize previously collected datasets from a variety of sources, including human demonstrations, prior experiments, domain-specific solutions and even data from different but related problems, to build complex decision-making engines.

Many tasks that we do on a regular basis, such as navigating a city, cooking a
meal, or loading a dishwasher, require planning over extended periods of time.
Accomplishing these tasks may seem simple to us; however, reasoning over long
time horizons remains a major challenge for today’s Reinforcement Learning (RL)
algorithms. While unable to plan over long horizons, deep RL algorithms excel
at learning policies for short horizon tasks, such as robotic grasping,
directly from pixels. At the same time, classical planning methods such as
Dijkstra’s algorithm and A$^*$ search can plan over long time horizons, but
they require hand-specified or task-specific abstract representations of the
environment as input.

To achieve the best of both worlds, state-of-the-art visual navigation methods
have applied classical search methods to learned graphs. In particular, SPTM [2] and SoRB [3] use a replay buffer of observations as nodes in a graph and learn
a parametric distance function to draw edges in the graph. These methods have
been successfully applied to long-horizon simulated navigation tasks that were
too challenging for previous methods to solve.

Multi-agent interacting systems are prevalent in the world, from purely physical systems to complicated social dynamic systems. The interactions between entities / components can give rise to very complex behavior patterns at the level of both individuals and the multi-agent system as a whole. Since usually only the trajectories of individual entities are observed without any knowledge of the underlying interaction patterns, and there are usually multiple possible modalities for each agent with uncertainty, it is challenging to model their dynamics and forecast their future behaviors.

Figure 1. Typical multi-agent interacting systems.

In many real-world applications (e.g. autonomous vehicles, mobile robots), an effective understanding of the situation and accurate trajectory prediction of interactive agents play a significant role in downstream tasks, such as decision making and planning. We introduce a generic trajectory forecasting framework (named EvolveGraph) with explicit relational structure recognition and prediction via latent interaction graphs among multiple heterogeneous, interactive agents. Considering the uncertainty of future behaviors, the model is designed to provide multi-modal prediction hypotheses. Since the underlying interactions may evolve even with abrupt changes over time, and different modalities of evolution may lead to different outcomes, we address the necessity of dynamic relational reasoning and adaptively evolving the interaction graphs.

Current machine learning methods provide unprecedented accuracy across a range
of domains, from computer vision to natural language processing. However, in
many important high-stakes applications, such as medical diagnosis or
autonomous driving, rare mistakes can be extremely costly, and thus effective
deployment of learned models requires not only high accuracy, but also a way to
measure the certainty in a model’s predictions. Reliable uncertainty
quantification is especially important when faced with out-of-distribution
inputs, as model accuracy tends to degrade heavily on inputs that differ
significantly from those seen during training. In this blog post, we will
discuss how we can get reliable uncertainty estimation with a strategy that
does not simply rely on a learned model to extrapolate to out-of-distribution
inputs, but instead asks: “given my training data, which labels would make
sense for this input?”.

Goodhart’s Law is an adage which states the following:

“When a measure becomes a target, it ceases to be a good measure.”

This is particularly pertinent in machine learning, where the source of many of
our greatest achievements comes from optimizing a target in the form of a loss
function. The most prominent way to do so is with stochastic gradient descent
(SGD), which applies a simple rule, follow the gradient:

For some step size $alpha$. Updates of this form have led to a series of
breakthroughs from computer vision to reinforcement learning, and it is easy to
see why it is so popular: 1) it is relatively cheap to compute using backprop
2) it is guaranteed to locally reduce the loss at every step and finally 3) it
has an amazing track record empirically.

Imagine that you are building the next generation machine learning model for handwriting transcription. Based on previous iterations of your product, you have identified a key challenge for this rollout: after deployment, new end users often have different and unseen handwriting styles, leading to distribution shift. One solution for this challenge is to learn an adaptive model that can specialize and adjust to each user’s handwriting style over time. This solution seems promising, but it must be balanced against concerns about ease of use: requiring users to provide feedback to the model may be cumbersome and hinder adoption. Is it possible instead to learn a model that can adapt to new users without labels?

The two most common perspectives on Reinforcement learning (RL) are optimization and dynamic programming. Methods that compute the gradients of the non-differentiable expected reward objective, such as the REINFORCE trick are commonly grouped into the optimization perspective, whereas methods that employ TD-learning or Q-learning are dynamic programming methods. While these methods have shown considerable success in recent years, these methods are still quite challenging to apply to new problems. In contrast deep supervised learning has been extremely successful and we may hence ask: Can we use supervised learning to perform RL?

In this blog post we discuss a mental model for RL, based on the idea that RL can be viewed as doing supervised learning on the “good data”. What makes RL challenging is that, unless you’re doing imitation learning, actually acquiring that “good data” is quite challenging. Therefore, RL might be viewed as a joint optimization problem over both the policy and the data. Seen from this supervised learning perspective, many RL algorithms can be viewed as alternating between finding good data and doing supervised learning on that data. It turns out that finding “good data” is much easier in the multi-task setting, or settings that can be converted to a different problem for which obtaining “good data” is easy. In fact, we will discuss how techniques such as hindsight relabeling and inverse RL can be viewed as optimizing data.

To operate successfully in unstructured open-world environments, autonomous
intelligent agents need to solve many different tasks and learn new tasks
quickly. Reinforcement learning has enabled artificial agents to solve complex
tasks both in simulation
and real-world.
However, it requires collecting large amounts of experience in the environment for each individual task. Self-supervised reinforcement learning has emerged
as an alternative,
where the agent only follows an intrinsic objective that is independent of any individual task,
analogously to unsupervised representation learning.
After acquiring general and reusable knowledge about the environment through
self-supervision, the agent can adapt to specific downstream tasks more
efficiently.

In this post, we explain our recent publication that develops Plan2Explore.
While many recent papers on self-supervised reinforcement learning have focused on
model-free agents, our agent learns an internal
world model that predicts the future outcomes of potential actions.
The world model captures general knowledge, allowing Plan2Explore to quickly solve new tasks through planning in its own imagination.
The world model further enables the agent to explore what it expects to be novel, rather than repeating what it found novel in the past.
Plan2Explore obtains state-of-the-art zero-shot and few-shot performance on continuous control benchmarks with high-dimensional input images.
To make it easy to experiment with our agent, we are open-sourcing the complete source code .

Our method learns complex behaviors by training offline from prior datasets
(expert demonstrations, data from previous experiments, or random exploration
data) and then fine-tuning quickly with online interaction.

Robots trained with reinforcement learning (RL) have the potential to be used
across a huge variety of challenging real world problems. To apply RL to a new
problem, you typically set up the environment, define a reward function, and
train the robot to solve the task by allowing it to explore the new environment
from scratch. While this may eventually work, these “online” RL methods are
data hungry and repeating this data inefficient process for every new problem
makes it difficult to apply online RL to real world robotics problems. What if
instead of repeating the data collection and learning process from scratch
every time, we were able to reuse data across multiple problems or experiments?
By doing so, we could greatly reduce the burden of data collection with every
new problem that is encountered. With hundreds to thousands of robot
experiments being constantly run, it is of crucial importance to devise an RL
paradigm that can effectively use the large amount of already available data
while still continuing to improve behavior on new tasks.

The first step towards moving RL towards a data driven paradigm is to consider
the general idea of offline (batch) RL. Offline RL considers the problem of
learning optimal policies from arbitrary off-policy data, without any further
exploration. This is able to eliminate the data collection problem in RL, and
incorporate data from arbitrary sources including other robots or
teleoperation. However, depending on the quality of available data and the
problem being tackled, we will often need to augment offline training with
targeted online improvement. This problem setting actually has unique
challenges of its own. In this blog post, we discuss how we can move RL from
training from scratch with every new problem to a paradigm which is able to
reuse prior data effectively, with some offline training followed by online
finetuning.