23,096 research outputs found
Virtual to Real Reinforcement Learning for Autonomous Driving
Reinforcement learning is considered as a promising direction for driving
policy learning. However, training autonomous driving vehicle with
reinforcement learning in real environment involves non-affordable
trial-and-error. It is more desirable to first train in a virtual environment
and then transfer to the real environment. In this paper, we propose a novel
realistic translation network to make model trained in virtual environment be
workable in real world. The proposed network can convert non-realistic virtual
image input into a realistic one with similar scene structure. Given realistic
frames as input, driving policy trained by reinforcement learning can nicely
adapt to real world driving. Experiments show that our proposed virtual to real
(VR) reinforcement learning (RL) works pretty well. To our knowledge, this is
the first successful case of driving policy trained by reinforcement learning
that can adapt to real world driving data
DoShiCo Challenge: Domain Shift in Control Prediction
Training deep neural network policies end-to-end for real-world applications
so far requires big demonstration datasets in the real world or big sets
consisting of a large variety of realistic and closely related 3D CAD models.
These real or virtual data should, moreover, have very similar characteristics
to the conditions expected at test time. These stringent requirements and the
time consuming data collection processes that they entail, are currently the
most important impediment that keeps deep reinforcement learning from being
deployed in real-world applications. Therefore, in this work we advocate an
alternative approach, where instead of avoiding any domain shift by carefully
selecting the training data, the goal is to learn a policy that can cope with
it. To this end, we propose the DoShiCo challenge: to train a model in very
basic synthetic environments, far from realistic, in a way that it can be
applied in more realistic environments as well as take the control decisions on
real-world data. In particular, we focus on the task of collision avoidance for
drones. We created a set of simulated environments that can be used as
benchmark and implemented a baseline method, exploiting depth prediction as an
auxiliary task to help overcome the domain shift. Even though the policy is
trained in very basic environments, it can learn to fly without collisions in a
very different realistic simulated environment. Of course several benchmarks
for reinforcement learning already exist - but they never include a large
domain shift. On the other hand, several benchmarks in computer vision focus on
the domain shift, but they take the form of a static datasets instead of
simulated environments. In this work we claim that it is crucial to take the
two challenges together in one benchmark.Comment: Published at SIMPAR 2018. Please visit the paper webpage for more
information, a movie and code for reproducing results:
https://kkelchte.github.io/doshic
Parallelized Interactive Machine Learning on Autonomous Vehicles
Deep reinforcement learning (deep RL) has achieved superior performance in
complex sequential tasks by learning directly from image input. A deep neural
network is used as a function approximator and requires no specific state
information. However, one drawback of using only images as input is that this
approach requires a prohibitively large amount of training time and data for
the model to learn the state feature representation and approach reasonable
performance. This is not feasible in real-world applications, especially when
the data are expansive and training phase could introduce disasters that affect
human safety. In this work, we use a human demonstration approach to speed up
training for learning features and use the resulting pre-trained model to
replace the neural network in the deep RL Deep Q-Network (DQN), followed by
human interaction to further refine the model. We empirically evaluate our
approach by using only a human demonstration model and modified DQN with human
demonstration model included in the Microsoft AirSim car simulator. Our results
show that (1) pre-training with human demonstration in a supervised learning
approach is better and much faster at discovering features than DQN alone, (2)
initializing the DQN with a pre-trained model provides a significant
improvement in training time and performance even with limited human
demonstration, and (3) providing the ability for humans to supply suggestions
during DQN training can speed up the network's convergence on an optimal
policy, as well as allow it to learn more complex policies that are harder to
discover by random exploration.Comment: 6 pages, NAECON 2018 - IEEE National Aerospace and Electronics
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