49 research outputs found
B-tests: Low Variance Kernel Two-Sample Tests
A family of maximum mean discrepancy (MMD) kernel two-sample tests is
introduced. Members of the test family are called Block-tests or B-tests, since
the test statistic is an average over MMDs computed on subsets of the samples.
The choice of block size allows control over the tradeoff between test power
and computation time. In this respect, the -test family combines favorable
properties of previously proposed MMD two-sample tests: B-tests are more
powerful than a linear time test where blocks are just pairs of samples, yet
they are more computationally efficient than a quadratic time test where a
single large block incorporating all the samples is used to compute a
U-statistic. A further important advantage of the B-tests is their
asymptotically Normal null distribution: this is by contrast with the
U-statistic, which is degenerate under the null hypothesis, and for which
estimates of the null distribution are computationally demanding. Recent
results on kernel selection for hypothesis testing transfer seamlessly to the
B-tests, yielding a means to optimize test power via kernel choice.Comment: Neural Information Processing Systems (2013
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
Simulations are attractive environments for training agents as they provide
an abundant source of data and alleviate certain safety concerns during the
training process. But the behaviours developed by agents in simulation are
often specific to the characteristics of the simulator. Due to modeling error,
strategies that are successful in simulation may not transfer to their real
world counterparts. In this paper, we demonstrate a simple method to bridge
this "reality gap". By randomizing the dynamics of the simulator during
training, we are able to develop policies that are capable of adapting to very
different dynamics, including ones that differ significantly from the dynamics
on which the policies were trained. This adaptivity enables the policies to
generalize to the dynamics of the real world without any training on the
physical system. Our approach is demonstrated on an object pushing task using a
robotic arm. Despite being trained exclusively in simulation, our policies are
able to maintain a similar level of performance when deployed on a real robot,
reliably moving an object to a desired location from random initial
configurations. We explore the impact of various design decisions and show that
the resulting policies are robust to significant calibration error
Overcoming Exploration in Reinforcement Learning with Demonstrations
Exploration in environments with sparse rewards has been a persistent problem
in reinforcement learning (RL). Many tasks are natural to specify with a sparse
reward, and manually shaping a reward function can result in suboptimal
performance. However, finding a non-zero reward is exponentially more difficult
with increasing task horizon or action dimensionality. This puts many
real-world tasks out of practical reach of RL methods. In this work, we use
demonstrations to overcome the exploration problem and successfully learn to
perform long-horizon, multi-step robotics tasks with continuous control such as
stacking blocks with a robot arm. Our method, which builds on top of Deep
Deterministic Policy Gradients and Hindsight Experience Replay, provides an
order of magnitude of speedup over RL on simulated robotics tasks. It is simple
to implement and makes only the additional assumption that we can collect a
small set of demonstrations. Furthermore, our method is able to solve tasks not
solvable by either RL or behavior cloning alone, and often ends up
outperforming the demonstrator policy.Comment: 8 pages, ICRA 201
Asymmetric Actor Critic for Image-Based Robot Learning
Deep reinforcement learning (RL) has proven a powerful technique in many
sequential decision making domains. However, Robotics poses many challenges for
RL, most notably training on a physical system can be expensive and dangerous,
which has sparked significant interest in learning control policies using a
physics simulator. While several recent works have shown promising results in
transferring policies trained in simulation to the real world, they often do
not fully utilize the advantage of working with a simulator. In this work, we
exploit the full state observability in the simulator to train better policies
which take as input only partial observations (RGBD images). We do this by
employing an actor-critic training algorithm in which the critic is trained on
full states while the actor (or policy) gets rendered images as input. We show
experimentally on a range of simulated tasks that using these asymmetric inputs
significantly improves performance. Finally, we combine this method with domain
randomization and show real robot experiments for several tasks like picking,
pushing, and moving a block. We achieve this simulation to real world transfer
without training on any real world data.Comment: Videos of experiments can be found at http://www.goo.gl/b57WT