Gradients

Understanding policy optimization and how it is used in reinforcement learningContinue reading on Towards Data Science » Understanding policy optimization and how it is used in reinforcement learningContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all…

Melting Away DNN’s Gradient Challenges: A Scoop of Solutions and InsightsContinue reading on Towards Data Science » Melting Away DNN’s Gradient Challenges: A Scoop of Solutions and InsightsContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all…

A gradient-based reinforcement learning algorithm to learn deterministic policies for continuous action spacesPhoto by Jonathan Ford on UnsplashThis article introduces Deep Deterministic Policy Gradient (DDPG) — a Reinforcement Learning algorithm suitable for deterministic policies applied in continuous action spaces. By combining the actor-critic paradigm with deep neural networks, continuous action spaces can be tackled without resorting to stochastic policies.Especially for continuous control tasks in which randomness…

Introduction and theoretical explanation of the DDPG algorithm, which has many applications in the field of continuous controlPhoto by Eyosias G on UnsplashThe DDPG algorithm, first presented at ICLR 2016 by Lillicarp et al. , was a significant breakthrough in terms of Deep Reinforcement Learning algorithms for continuous control, because of its improvement over DQN (which only works with discrete actions), and its very good results and ease of implementation (see ).As for the NAF algorithm presented in the previous…

Keras does a great job of abstracting low-level details of neural network creation so you can focus on getting the job done. But, if you’re reading this, you’ve probably discovered that Keras’ off-the-shelf methods cannot always be used to learn your model’s parameters. Perhaps your model has a gradient that cannot be calculated through the magic of autodiff, or your loss function does not conform to the signature my_loss_fn(y_true, y_pred) mentioned in Keras’ documentation. If you found the online documentation wholly…

Traditional policy gradient methods are fundamentally flawed. Natural gradients converge quicker and better, forming the foundation of contemporary Reinforcement Learning algorithms.Natural policy gradients progress policies on a statistical manifold, ensuring consistent updates of the same Riemannian distance. Policy gradient algorithms are at the root of modern Reinforcement Learning. The idea is that, by simply following the gradient (i.e., vector of partial derivatives) of the objective function, we ultimately end up…