Modular deep reinforcement learning for continuous motion planning with temporal logic
… continuous action space to improve the learning performance [18]. In this letter, we consider
motion planning under LTL task specifications in continuous … DDPG-based motion planning …
motion planning under LTL task specifications in continuous … DDPG-based motion planning …
Survey of deep reinforcement learning for motion planning of autonomous vehicles
S Aradi - IEEE Transactions on Intelligent Transportation …, 2020 - ieeexplore.ieee.org
… There exists only one exception in [123], where the authors taught the agent for lane following
on a real vehicle using a continuous, model-free deep reinforcement learning algorithm …
on a real vehicle using a continuous, model-free deep reinforcement learning algorithm …
A survey of deep reinforcement learning algorithms for motion planning and control of autonomous vehicles
… deep RL algorithms can be leveraged to accomplish behavioral decision making, motion
planning and control modules in the … makings and continuous motion planning tasks [15], [16]. …
planning and control modules in the … makings and continuous motion planning tasks [15], [16]. …
A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures
… MP module consists of the global motion planner and the … hyperparameters in classical motion
planners. The second section … the hierarchical learning paradigm and continuous learning …
planners. The second section … the hierarchical learning paradigm and continuous learning …
Learning to navigate through complex dynamic environment with modular deep reinforcement learning
… for obstacle avoidance and path planning for mobile robots in … based robot with continuous
control using asynchronous deep … [41] developed a hierarchical deep reinforcement learning …
control using asynchronous deep … [41] developed a hierarchical deep reinforcement learning …
Wisemove: A framework for safe deep reinforcement learning for autonomous driving
J Lee, A Balakrishnan, A Gaurav, K Czarnecki… - arXiv preprint arXiv …, 2019 - arxiv.org
… the safety and scalability of motion planning, where safety can … We have thus devised a
hierarchical and modular DRL framework… a deep neural network to encode the continuous action …
hierarchical and modular DRL framework… a deep neural network to encode the continuous action …
Continuous motion planning with temporal logic specifications using deep neural networks
… In this section, we introduce our method of solving a continuous state and action MDP with
LTL specifications using deep reinforcement learning. The LTL specification is transformed …
LTL specifications using deep reinforcement learning. The LTL specification is transformed …
Integrating deep reinforcement learning with model-based path planners for automated driving
… In summary, the proposed method integrates a short pipeline of localization and path planning
modules into a DRL driving agent. The training goal is to teach the DRL agent to oversee …
modules into a DRL driving agent. The training goal is to teach the DRL agent to oversee …
Modular deep reinforcement learning with temporal logic specifications
… Modular Deep RL We consider a modular deep RL problem in which we exploit the …
Given an LTL mission task and an unknown continuousstate continuous-action MDP, we aim to …
Given an LTL mission task and an unknown continuousstate continuous-action MDP, we aim to …
Decentralized motion planning for multi-robot navigation using deep reinforcement learning
K Sivanathan, BK Vinayagam… - 2020 3rd International …, 2020 - ieeexplore.ieee.org
… it much more flexible and modular to work with than any other … continuous, to challenge the
agents by providing them with bare minimal information to accomplish the task of continuous …
agents by providing them with bare minimal information to accomplish the task of continuous …
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