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NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control

2020-11-02 · Nan Lin, YuXuan Li, Yujun Zhu, Ruolin Wang, Xiayu Zhang, Jianmin Ji, Keke Tang, Xiaoping Chen, Xinming Zhang

Traditionally, reinforcement learning methods predict the next action based on the current state. However, in many situations, directly applying actions to control systems or robots is dangerous and may lead to unexpected behaviors because action is rather low-level. In this paper, we propose a novel hierarchical reinforcement learning framework without explicit action. Our meta policy tries to manipulate the next optimal state and actual action is produced by the inverse dynamics model. To stabilize the training process, we integrate adversarial learning and information bottleneck into our framework. Under our framework, widely available state-only demonstrations can be exploited effectively for imitation learning. Also, prior knowledge and constraints can be applied to meta policy. We test our algorithm in simulation tasks and its combination with imitation learning. The experimental results show the reliability and robustness of our algorithms.

📄 PDF Abstract BibTeX arXiv:2011.01046

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Hierarchical Reinforcement LearningImitation Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Nightmare Dreamer: Dreaming About Unsafe States And Planning Ahead

2026-01-08 · Oluwatosin Oseni, Shengjie Wang, Jun Zhu, Micah Corah arxiv

Reinforcement Learning (RL) has shown remarkable success in real-world applications, particularly in robotics control. However, RL adoption remains limited due to insufficient safety guarantees. We introduce Nightmare Dr…

Reinforcement Learning

Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known Dynamics

2017-06-04 · Tomoki Nishi, Prashant Doshi, Michael R. James, Danil Prokhorov

In many robotic applications, some aspects of the system dynamics can be modeled accurately while others are difficult to obtain or model. We present a novel reinforcement learning (RL) method for continuous state and ac…

Reinforcement Learning Reinforcement Learning (RL)

Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems

2019-01-24 · Osbert Bastani

Reinforcement learning is a promising approach to learning robotics controllers. It has recently been shown that algorithms based on finite-difference estimates of the policy gradient are competitive with algorithms base…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

A Reinforcement Learning Neural Network for Robotic Manipulator Control

2018-07-01 · Conference 2018 7 · Yazhou Hu, Bailu Si

We propose a neural network model for reinforcement learning to control a robotic manipulator with unknown parameters and dead zones. The model is composed of three networks. The state of the robotic manipulator is predi…