[PDF] Top 20 Flow: Deep Reinforcement Learning for Control in SUMO

... respectively. Reinforcement learning-based methods of traffic control using vehicles arrive upon similar results to those theoretically derived as in [7], in both the one- and two-lane ... See full document

... Deep reinforcement learning (DRL) is a general-purpose framework for decision mak- ing based on representation ...include deep Q- learning (Mnih et al., 2015), deep visuomotor ... See full document

... in deep reinforcement learning for swarms and multi-agent systems in ...many-agent reinforcement learning platform based on a multi-channel image state representation, which uses ... See full document

... Deep learning is set to transform the arena of artificial intelligence as well as represent a measure in the direction of developing autonomous systems with an increased scope of perceiving the visual ... See full document

... unfamiliar player populations. A narrative planning policy’s generalizability is essential to consider because the deployment environment of an interactive narrative planner is often challenging to control, and it ... See full document

... pixel control (Jaderberg et ...Pixel control is an unsuper- vised auxiliary task introduced to help learning good state ...pixel control (red line) matched the final performance of the vanilla ... See full document

... in deep learning (Le- Cun et ...combining deep learning with reinforcement ...for learning control policies for parser- based text ...continuous control with ... See full document

... acceptable) control policy is often the primary objective in combining machine learning with ...using deep neural networks for learning a control policy is deep Q-learning ... See full document

... A limited number of research works present architectures similar to JNN. Although they also combined scalar and image inputs in a neural network, this was never used for the RL state. For instance, in the hybrid reward ... See full document

... transfer learning, show- ing that pre-training portions of the deep Q- network using question answering system on per- fect playthroughs of a game increases the qual- ity of the learned control ... See full document

... proposed learning approaches do exist that enable a robot to learn naviga- tion actions using on-board sensory information in environments with known and unknown flat ...on deep reinforcement ... See full document

... Problem I What is the meaning of the assumption in the total flow of data ? Last in this section let us comment on the minibatch of Deep Learning. When input data A is huge the calculation of time ... See full document

... on deep reinforcement learning suggested in this article, we implemented DDPG basing on previous ...A deep reinforcement learning environment was built basing on Python, the ... See full document

... when learning on systems with a limited storage capacity, for instance when dealing with high-dimensional inputs such as ...our control (Seo and Zhang, 2000; Schaal, ... See full document

... The features chosen are such that the global policy has information about the candidate sen- tence (1), the entire list of candidate sentences (2), the summary that has been generated so far (3), the input sentences that ... See full document

... Reinforcement Learning. Deep Deterministic Policy Gra- dient algorithm (DDPG) (Lillicrap et ...using deep neural networks to approximate the action-value function for improving ...conventional ... See full document

... Reinforcement Learning has initially made it possible to solve a large variety of tasks through hand-crafted features and state representations, often limited by small state or action spaces [2, 3], with ... See full document

... popular reinforcement learning algorithms, and the goal of reinforcement learning [1, 2] is a good strategy for learning continuous decision-making problems by optimizing the ... See full document

... machine learning models for Chinese zero pronoun resolution have been ...machine learning tech- niques are applied for this ...(2) learning-based models (Iida and Poesio, 2011; Isozaki and Hirao, ... See full document

... Mention-ranking models are typically trained with heuristic loss functions that are tuned via hyperpa- rameters. These hyperparameters are usually given as costs for different error types, which are used to bias the ... See full document