2,864 research outputs found
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer
An important goal of research in Deep Reinforcement Learning in mobile
robotics is to train agents capable of solving complex tasks, which require a
high level of scene understanding and reasoning from an egocentric perspective.
When trained from simulations, optimal environments should satisfy a currently
unobtainable combination of high-fidelity photographic observations, massive
amounts of different environment configurations and fast simulation speeds. In
this paper we argue that research on training agents capable of complex
reasoning can be simplified by decoupling from the requirement of high fidelity
photographic observations. We present a suite of tasks requiring complex
reasoning and exploration in continuous, partially observable 3D environments.
The objective is to provide challenging scenarios and a robust baseline agent
architecture that can be trained on mid-range consumer hardware in under 24h.
Our scenarios combine two key advantages: (i) they are based on a simple but
highly efficient 3D environment (ViZDoom) which allows high speed simulation
(12000fps); (ii) the scenarios provide the user with a range of difficulty
settings, in order to identify the limitations of current state of the art
algorithms and network architectures. We aim to increase accessibility to the
field of Deep-RL by providing baselines for challenging scenarios where new
ideas can be iterated on quickly. We argue that the community should be able to
address challenging problems in reasoning of mobile agents without the need for
a large compute infrastructure
Atari games and Intel processors
The asynchronous nature of the state-of-the-art reinforcement learning
algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes
them exceptionally suitable for CPU computations. However, given the fact that
deep reinforcement learning often deals with interpreting visual information, a
large part of the train and inference time is spent performing convolutions. In
this work we present our results on learning strategies in Atari games using a
Convolutional Neural Network, the Math Kernel Library and TensorFlow 0.11rc0
machine learning framework. We also analyze effects of asynchronous
computations on the convergence of reinforcement learning algorithms
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