People give massive amounts of their personal data to companies every day and
these data are used to generate tremendous business values. Some
economists
and
politicians
argue that people should be paid for their contributions—but the
million-dollar question is: by how much?
This article discusses methods proposed in our recent
AISTATS and
VLDB papers that attempt to answer this
question in the machine learning context. This is joint work with David Dao,
Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Nick Hynes, Bo Li, Ce Zhang,
Costas J. Spanos, and Dawn Song, as well as a collaborative effort between UC
Berkeley, ETH Zurich, and UIUC. More information about the work in our group
can be found here.
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This work presents AVID, a method that allows a robot to learn a task, such as
making coffee, directly by watching a human perform the task.
One of the most important markers of intelligence is the ability to learn by
watching others. Humans are particularly good at this, often being able to
learn tasks by observing other humans. This is possible because we are not
simply copying the actions that other humans take. Rather, we first imagine
ourselves performing the task, and this provides a starting point for further
practicing the task in the real world.
Robots are not yet adept at learning by watching humans or other robots. Prior
methods for imitation learning, where robots learn from demonstrations of the
task, typically assume that the demonstrations can be given directly through
the robot, using techniques such as kinesthetic
teaching or
teleoperation. This assumption limits
the applicability of robots in the real world, where robots may be frequently
asked to learn new tasks quickly and without programmers, trained roboticists,
or specialized hardware setups. Can we instead have robots learn directly from
a video of a human demonstration?
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Reinforcement learning systems can make decisions in one of two ways. In the model-based approach, a system uses a predictive model of the world to ask questions of the form “what will happen if I do x?” to choose the best x. In the alternative model-free approach, the modeling step is bypassed altogether in favor of learning a control policy directly. Although in practice the line between these two techniques can become blurred, as a coarse guide it is useful for dividing up the space of algorithmic possibilities.
Predictive models can be used to ask “what if?” questions to guide future decisions.
The natural question to ask after making this distinction is whether to use such a predictive model. The field has grappled with this question for quite a while, and is unlikely to reach a consensus any time soon. However, we have learned enough about designing model-based algorithms that it is possible to draw some general conclusions about best practices and common pitfalls. In this post, we will survey various realizations of model-based reinforcement learning methods. We will then describe some of the tradeoffs that come into play when using a learned predictive model for training a policy and how these considerations motivate a simple but effective strategy for model-based reinforcement learning. The latter half of this post is based on our recent paper on model-based policy optimization, for which code is available here.
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One of the primary factors behind the success of machine learning approaches in open world settings, such as image recognition and natural language processing, has been the ability of high-capacity deep neural network function approximators to learn generalizable models from large amounts of data. Deep reinforcement learning methods, however, require active online data collection, where the model actively interacts with its environment. This makes such methods hard to scale to complex real-world problems, where active data collection means that large datasets of experience must be collected for every experiment – this can be expensive and, for systems such as autonomous vehicles or robots, potentially unsafe. In a number of domains of practical interest, such as autonomous driving, robotics, and games, there exist plentiful amounts of previously collected interaction data which, consists of informative behaviours that are a rich source of prior information. Deep RL algorithms that can utilize such prior datasets will not only scale to real-world problems, but will also lead to solutions that generalize substantially better. A data-driven paradigm for reinforcement learning will enable us to pre-train and deploy agents capable of sample-efficient learning in the real-world.
In this work, we ask the following question: Can deep RL algorithms effectively leverage prior collected offline data and learn without interaction with the environment? We refer to this problem statement as fully off-policy RL, previously also called batch RL in literature. A class of deep RL algorithms, known as off-policy RL algorithms can, in principle, learn from previously collected data. Recent off-policy RL algorithms such as Soft Actor-Critic (SAC), QT-Opt, and Rainbow, have demonstrated sample-efficient performance in a number of challenging domains such as robotic manipulation and atari games. However, all of these methods still require online data collection, and their ability to learn from fully off-policy data is limited in practice. In this work, we show why existing deep RL algorithms can fail in the fully off-policy setting. We then propose effective solutions to mitigate these issues.
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This post is cross-listed at the
SAIL Blog and the
CMU ML blog.
In the last decade, we’ve seen learning-based systems provide transformative
solutions for a wide range of perception and reasoning problems, from
recognizing objects in
images
to recognizing and translating human
speech.
Recent progress in deep reinforcement learning (i.e. integrating deep neural
networks into reinforcement learning systems) suggests that the same kind of
success could be realized in automated decision making domains. If fruitful,
this line of work could allow learning-based systems to tackle active control
tasks, such as robotics and autonomous driving, alongside the passive
perception tasks to which they have already been successfully applied.
While deep reinforcement learning methods - like Soft Actor
Critic - can learn impressive
motor skills, they are challenging to train on large and broad data that is not
from the target environment. In contrast, the success of deep networks in
fields like computer vision was arguably predicated just as much on large
datasets, such as ImageNet, as it was on large
neural network architectures. This suggests that applying data-driven methods
to robotics will require not just the development of strong reinforcement
learning methods, but also access to large and diverse datasets for robotics.
Not only can large datasets enable models that generalize effectively, but they
can also be used to pre-train models that can then be adapted to more
specialized tasks using much more modest datasets. Indeed, “ImageNet
pre-training” has become a default approach for tackling diverse tasks with
small or medium datasets - like 3D building
reconstruction.
Can the same kind of approach be adopted to enable broad generalization and
transfer in active control domains, such as robotics?
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Nov 22, 2019
Prof. Anca Dragan
gave a talk as part of the WIRED25 summit, explaining some
of the challenges robots face when interacting with people. First, robots that
share space with people, from autonomous cars to quadrotors to indoor mobile
robots, need to anticipate what people plan on doing and make sure they can
stay out of the way. This is already hard, because robots are not mind readers,
and yet they need access to a rough simulator of us, humans, that they can use
to help them decide how to act. The bar gets raised when it’s crowded, because
then robots have to also understand how they can influence the actions that
people take, like getting another driver to slow down and make space for a
merging autonomous car. And what if the person decides to accelerate instead?
Find out about the ways in which robots can negotiate these situations in the
video below.
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The incredible success of BERT in Natural Language Processing (NLP) showed that large models trained on unlabeled data are able to learn powerful representations of language. These representations have been shown to encode information about syntax and semantics. In this blog post we ask the question: Can similar methods be applied to biological sequences, specifically proteins? If so, to what degree do they improve performance on protein prediction problems that are relevant to biologists?
We discuss our recent work on TAPE: Tasks Assessing Protein Embeddings (preprint) (github), a benchmarking suite for protein representations learned by various neural architectures and self-supervised losses. We also discuss the challenges that proteins present to the ML community, previously described by xkcd:
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When learning to follow natural language instructions, neural networks tend to
be very data hungry – they require a huge number of examples pairing language
with actions in order to learn effectively. This post is about reducing those
heavy data requirements by first watching actions in the environment before
moving on to learning from language data. Inspired by the idea that it is
easier to map language to meanings that have already been formed, we introduce
a semi-supervised approach that aims to separate the formation of abstractions
from the learning of language. Empirically, we find that pre-learning of
patterns in the environment can help us learn grounded language with much less
data.
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AI agents have learned to play Dota, StarCraft, and Go, by training to beat an
automated system that increases in difficulty as the agent gains skill at the
game: in vanilla self-play, the AI agent plays games against itself, while in
population-based training, each agent must play against a population of other
agents, and the entire population learns to play the game.
This technique has a lot going for it. There is a natural curriculum in
difficulty: as the agent improves, the task it faces gets harder, which leads
to efficient learning. It doesn’t require any manual design of opponents, or
handcrafted features of the environment. And most notably, in all of the games
above, the resulting agents have beaten human champions.
The technique has also been used in collaborative settings: OpenAI had one
public match where each team was composed of three OpenAI Five agents alongside
two human experts, and the For The Win (FTW) agents trained to play Quake were
paired with both humans and other agents during evaluation. In the Quake
case, humans rated the FTW agents as more collaborative than fellow humans
in a participant survey.
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In this blog post, we explore a functional paradigm for implementing
reinforcement learning
(RL) algorithms. The paradigm will be that developers write the numerics of
their algorithm as independent, pure functions, and then use a library to
compile them into policies that can be trained at scale. We share how these
ideas were implemented in RLlib’s policy builder
API,
eliminating thousands of lines of “glue” code and bringing support for
Keras
and TensorFlow
2.0.
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