6,839 research outputs found
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control
policies directly from high-dimensional sensory input using reinforcement
learning. The model is a convolutional neural network, trained with a variant
of Q-learning, whose input is raw pixels and whose output is a value function
estimating future rewards. We apply our method to seven Atari 2600 games from
the Arcade Learning Environment, with no adjustment of the architecture or
learning algorithm. We find that it outperforms all previous approaches on six
of the games and surpasses a human expert on three of them.Comment: NIPS Deep Learning Workshop 201
Natural evolution strategies and variational Monte Carlo
A notion of quantum natural evolution strategies is introduced, which
provides a geometric synthesis of a number of known quantum/classical
algorithms for performing classical black-box optimization. Recent work of
Gomes et al. [2019] on heuristic combinatorial optimization using neural
quantum states is pedagogically reviewed in this context, emphasizing the
connection with natural evolution strategies. The algorithmic framework is
illustrated for approximate combinatorial optimization problems, and a
systematic strategy is found for improving the approximation ratios. In
particular it is found that natural evolution strategies can achieve
approximation ratios competitive with widely used heuristic algorithms for
Max-Cut, at the expense of increased computation time
Do optimization methods in deep learning applications matter?
With advances in deep learning, exponential data growth and increasing model
complexity, developing efficient optimization methods are attracting much
research attention. Several implementations favor the use of Conjugate Gradient
(CG) and Stochastic Gradient Descent (SGD) as being practical and elegant
solutions to achieve quick convergence, however, these optimization processes
also present many limitations in learning across deep learning applications.
Recent research is exploring higher-order optimization functions as better
approaches, but these present very complex computational challenges for
practical use. Comparing first and higher-order optimization functions, in this
paper, our experiments reveal that Levemberg-Marquardt (LM) significantly
supersedes optimal convergence but suffers from very large processing time
increasing the training complexity of both, classification and reinforcement
learning problems. Our experiments compare off-the-shelf optimization
functions(CG, SGD, LM and L-BFGS) in standard CIFAR, MNIST, CartPole and
FlappyBird experiments.The paper presents arguments on which optimization
functions to use and further, which functions would benefit from
parallelization efforts to improve pretraining time and learning rate
convergence
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning
Recent advances in combining deep neural network architectures with
reinforcement learning techniques have shown promising potential results in
solving complex control problems with high dimensional state and action spaces.
Inspired by these successes, in this paper, we build two kinds of reinforcement
learning algorithms: deep policy-gradient and value-function based agents which
can predict the best possible traffic signal for a traffic intersection. At
each time step, these adaptive traffic light control agents receive a snapshot
of the current state of a graphical traffic simulator and produce control
signals. The policy-gradient based agent maps its observation directly to the
control signal, however the value-function based agent first estimates values
for all legal control signals. The agent then selects the optimal control
action with the highest value. Our methods show promising results in a traffic
network simulated in the SUMO traffic simulator, without suffering from
instability issues during the training process
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