3,835 research outputs found
Transfer from Multiple MDPs
Transfer reinforcement learning (RL) methods leverage on the experience
collected on a set of source tasks to speed-up RL algorithms. A simple and
effective approach is to transfer samples from source tasks and include them
into the training set used to solve a given target task. In this paper, we
investigate the theoretical properties of this transfer method and we introduce
novel algorithms adapting the transfer process on the basis of the similarity
between source and target tasks. Finally, we report illustrative experimental
results in a continuous chain problem.Comment: 201
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
Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning
Many problems in sequential decision making and stochastic control often have
natural multiscale structure: sub-tasks are assembled together to accomplish
complex goals. Systematically inferring and leveraging hierarchical structure,
particularly beyond a single level of abstraction, has remained a longstanding
challenge. We describe a fast multiscale procedure for repeatedly compressing,
or homogenizing, Markov decision processes (MDPs), wherein a hierarchy of
sub-problems at different scales is automatically determined. Coarsened MDPs
are themselves independent, deterministic MDPs, and may be solved using
existing algorithms. The multiscale representation delivered by this procedure
decouples sub-tasks from each other and can lead to substantial improvements in
convergence rates both locally within sub-problems and globally across
sub-problems, yielding significant computational savings. A second fundamental
aspect of this work is that these multiscale decompositions yield new transfer
opportunities across different problems, where solutions of sub-tasks at
different levels of the hierarchy may be amenable to transfer to new problems.
Localized transfer of policies and potential operators at arbitrary scales is
emphasized. Finally, we demonstrate compression and transfer in a collection of
illustrative domains, including examples involving discrete and continuous
statespaces.Comment: 86 pages, 15 figure
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