Reinforcement Learning with External Knowledge – In this paper, we show what happens when we learn a model of the object-oriented game, and then interact with the environment with external knowledge. To capture the internal knowledge in the game, we propose three deep reinforcement learning approaches: (1) reinforcement learning; (2) reinforcement learning using external knowledge; (3) reinforcement learning using internal knowledge. We show that this is a more efficient strategy than reinforcement learning in the worst case. We have applied our approach to the problem and obtained competitive results compared to reinforcement learning. For example, in the worst case we achieved higher overall test performance. We further extend our approach with one more step. These two steps are complementary and have the same performance in terms of test performance. Finally, we present a framework for learning in adversarial environments to learn the state and action information, and for learning the object behavior and the environment.

One of the great success of computer vision has been its ability to map the real world environment so that it can be reconstructed, but it does not provide a means for exploring the potential of such a system. We show how to map a given environment to its underlying representations, for example by using convolutional networks to solve the Bethe Equation Prover. Our approach involves building a new image representation of the environment. We demonstrate how these representations are combined with a generic representation of the target spatial plane, and in this representation we are able to generate object trajectories along the horizon and to find optimal trajectory paths. Using a convolutional neural network (CNN), we achieve state-of-the-art precision and accuracy on our problem with an extremely high success rate.

Probabilistic Belief Propagation by Differential Evolution

A Bayesian non-weighted loss function to augment and expand the learning rate

Reinforcement Learning with External Knowledge

Image denoising by additive fog light using a deep dictionary

The Bethe Equation ProverOne of the great success of computer vision has been its ability to map the real world environment so that it can be reconstructed, but it does not provide a means for exploring the potential of such a system. We show how to map a given environment to its underlying representations, for example by using convolutional networks to solve the Bethe Equation Prover. Our approach involves building a new image representation of the environment. We demonstrate how these representations are combined with a generic representation of the target spatial plane, and in this representation we are able to generate object trajectories along the horizon and to find optimal trajectory paths. Using a convolutional neural network (CNN), we achieve state-of-the-art precision and accuracy on our problem with an extremely high success rate.