Ameya Divekar

Using Deep Reinforcement Learning for autonomous system control entails finding the right combination of parameters for achieving optimal quality. Finding global optimum is often challenged by several factors such as high dimensionality, non-linearity and noisy state and action spaces along with convergence issues. Conditional generative adversarial networks (C-GANs) are used to learn the underlying distribution of the solutions generated by the optimization algorithm, and then generating unseen, more optimized solutions to the original optimization problem using the generative model. These solutions are used to sample the episodes for training the agent in subsequent episodes thereby reducing the variance and enhancing the probability of more optimal solutions.