67,943 research outputs found
VPE: Variational Policy Embedding for Transfer Reinforcement Learning
Reinforcement Learning methods are capable of solving complex problems, but
resulting policies might perform poorly in environments that are even slightly
different. In robotics especially, training and deployment conditions often
vary and data collection is expensive, making retraining undesirable.
Simulation training allows for feasible training times, but on the other hand
suffers from a reality-gap when applied in real-world settings. This raises the
need of efficient adaptation of policies acting in new environments. We
consider this as a problem of transferring knowledge within a family of similar
Markov decision processes.
For this purpose we assume that Q-functions are generated by some
low-dimensional latent variable. Given such a Q-function, we can find a master
policy that can adapt given different values of this latent variable. Our
method learns both the generative mapping and an approximate posterior of the
latent variables, enabling identification of policies for new tasks by
searching only in the latent space, rather than the space of all policies. The
low-dimensional space, and master policy found by our method enables policies
to quickly adapt to new environments. We demonstrate the method on both a
pendulum swing-up task in simulation, and for simulation-to-real transfer on a
pushing task
Balancing the Communication Load of Asynchronously Parallelized Machine Learning Algorithms
Stochastic Gradient Descent (SGD) is the standard numerical method used to
solve the core optimization problem for the vast majority of machine learning
(ML) algorithms. In the context of large scale learning, as utilized by many
Big Data applications, efficient parallelization of SGD is in the focus of
active research. Recently, we were able to show that the asynchronous
communication paradigm can be applied to achieve a fast and scalable
parallelization of SGD. Asynchronous Stochastic Gradient Descent (ASGD)
outperforms other, mostly MapReduce based, parallel algorithms solving large
scale machine learning problems. In this paper, we investigate the impact of
asynchronous communication frequency and message size on the performance of
ASGD applied to large scale ML on HTC cluster and cloud environments. We
introduce a novel algorithm for the automatic balancing of the asynchronous
communication load, which allows to adapt ASGD to changing network bandwidths
and latencies.Comment: arXiv admin note: substantial text overlap with arXiv:1505.0495
Agile Autonomous Driving using End-to-End Deep Imitation Learning
We present an end-to-end imitation learning system for agile, off-road
autonomous driving using only low-cost sensors. By imitating a model predictive
controller equipped with advanced sensors, we train a deep neural network
control policy to map raw, high-dimensional observations to continuous steering
and throttle commands. Compared with recent approaches to similar tasks, our
method requires neither state estimation nor on-the-fly planning to navigate
the vehicle. Our approach relies on, and experimentally validates, recent
imitation learning theory. Empirically, we show that policies trained with
online imitation learning overcome well-known challenges related to covariate
shift and generalize better than policies trained with batch imitation
learning. Built on these insights, our autonomous driving system demonstrates
successful high-speed off-road driving, matching the state-of-the-art
performance.Comment: 13 pages, Robotics: Science and Systems (RSS) 201
How a well-adapting immune system remembers
An adaptive agent predicting the future state of an environment must weigh
trust in new observations against prior experiences. In this light, we propose
a view of the adaptive immune system as a dynamic Bayesian machinery that
updates its memory repertoire by balancing evidence from new pathogen
encounters against past experience of infection to predict and prepare for
future threats. This framework links the observed initial rapid increase of the
memory pool early in life followed by a mid-life plateau to the ease of
learning salient features of sparse environments. We also derive a modulated
memory pool update rule in agreement with current vaccine response experiments.
Our results suggest that pathogenic environments are sparse and that memory
repertoires significantly decrease infection costs even with moderate sampling.
The predicted optimal update scheme maps onto commonly considered competitive
dynamics for antigen receptors
Trajectory-Based Off-Policy Deep Reinforcement Learning
Policy gradient methods are powerful reinforcement learning algorithms and
have been demonstrated to solve many complex tasks. However, these methods are
also data-inefficient, afflicted with high variance gradient estimates, and
frequently get stuck in local optima. This work addresses these weaknesses by
combining recent improvements in the reuse of off-policy data and exploration
in parameter space with deterministic behavioral policies. The resulting
objective is amenable to standard neural network optimization strategies like
stochastic gradient descent or stochastic gradient Hamiltonian Monte Carlo.
Incorporation of previous rollouts via importance sampling greatly improves
data-efficiency, whilst stochastic optimization schemes facilitate the escape
from local optima. We evaluate the proposed approach on a series of continuous
control benchmark tasks. The results show that the proposed algorithm is able
to successfully and reliably learn solutions using fewer system interactions
than standard policy gradient methods.Comment: Includes appendix. Accepted for ICML 201
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