13,040 research outputs found
Using Hindsight to Anchor Past Knowledge in Continual Learning
In continual learning, the learner faces a stream of data whose distribution
changes over time. Modern neural networks are known to suffer under this
setting, as they quickly forget previously acquired knowledge. To address such
catastrophic forgetting, many continual learning methods implement different
types of experience replay, re-learning on past data stored in a small buffer
known as episodic memory. In this work, we complement experience replay with a
new objective that we call anchoring, where the learner uses bilevel
optimization to update its knowledge on the current task, while keeping intact
the predictions on some anchor points of past tasks. These anchor points are
learned using gradient-based optimization to maximize forgetting, which is
approximated by fine-tuning the currently trained model on the episodic memory
of past tasks. Experiments on several supervised learning benchmarks for
continual learning demonstrate that our approach improves the standard
experience replay in terms of both accuracy and forgetting metrics and for
various sizes of episodic memories.Comment: Accepted at AAAI 202
Combining Experience Replay with Exploration by Random Network Distillation
Our work is a simple extension of the paper "Exploration by Random Network
Distillation". More in detail, we show how to efficiently combine Intrinsic
Rewards with Experience Replay in order to achieve more efficient and robust
exploration (with respect to PPO/RND) and consequently better results in terms
of agent performances and sample efficiency. We are able to do it by using a
new technique named Prioritized Oversampled Experience Replay (POER), that has
been built upon the definition of what is the important experience useful to
replay. Finally, we evaluate our technique on the famous Atari game Montezuma's
Revenge and some other hard exploration Atari games.Comment: 8 pages, 6 figures, accepted as full-paper at IEEE Conference on
Games (CoG) 201
Learning Adaptive Display Exposure for Real-Time Advertising
In E-commerce advertising, where product recommendations and product ads are
presented to users simultaneously, the traditional setting is to display ads at
fixed positions. However, under such a setting, the advertising system loses
the flexibility to control the number and positions of ads, resulting in
sub-optimal platform revenue and user experience. Consequently, major
e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible
ways to display ads. In this paper, we investigate the problem of advertising
with adaptive exposure: can we dynamically determine the number and positions
of ads for each user visit under certain business constraints so that the
platform revenue can be increased? More specifically, we consider two types of
constraints: request-level constraint ensures user experience for each user
visit, and platform-level constraint controls the overall platform monetization
rate. We model this problem as a Constrained Markov Decision Process with
per-state constraint (psCMDP) and propose a constrained two-level reinforcement
learning approach to decompose the original problem into two relatively
independent sub-problems. To accelerate policy learning, we also devise a
constrained hindsight experience replay mechanism. Experimental evaluations on
industry-scale real-world datasets demonstrate the merits of our approach in
both obtaining higher revenue under the constraints and the effectiveness of
the constrained hindsight experience replay mechanism.Comment: accepted by CIKM201
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search
In principle, reinforcement learning and policy search methods can enable
robots to learn highly complex and general skills that may allow them to
function amid the complexity and diversity of the real world. However, training
a policy that generalizes well across a wide range of real-world conditions
requires far greater quantity and diversity of experience than is practical to
collect with a single robot. Fortunately, it is possible for multiple robots to
share their experience with one another, and thereby, learn a policy
collectively. In this work, we explore distributed and asynchronous policy
learning as a means to achieve generalization and improved training times on
challenging, real-world manipulation tasks. We propose a distributed and
asynchronous version of Guided Policy Search and use it to demonstrate
collective policy learning on a vision-based door opening task using four
robots. We show that it achieves better generalization, utilization, and
training times than the single robot alternative.Comment: Submitted to the IEEE International Conference on Robotics and
Automation 201
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