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Found 9 papers, 0 papers with code
Actor-Critic Reinforcement Learning with Phased Actor
We prove qualitative properties of PAAC for learning convergence of the value and policy, solution optimality, and stability of system dynamics.
Mitigating Estimation Errors by Twin TD-Regularized Actor and Critic for Deep Reinforcement Learning
We address the issue of estimation bias in deep reinforcement learning (DRL) by introducing solution mechanisms that include a new, twin TD-regularized actor-critic (TDR) method.
Modulation-Enhanced Excitation for Continuous-Time Reinforcement Learning via Symmetric Kronecker Products
This work introduces new results in continuous-time reinforcement learning (CT-RL) control of affine nonlinear systems to address a major algorithmic challenge due to a lack of persistence of excitation (PE).
Continuous-Time Reinforcement Learning: New Design Algorithms with Theoretical Insights and Performance Guarantees
The goal of this work is thus to introduce a suite of new CT-RL algorithms for control of affine nonlinear systems.
Long N-step Surrogate Stage Reward to Reduce Variances of Deep Reinforcement Learning in Complex Problems
However, there is a lack of comprehensive and systematic study on this important aspect to demonstrate the effectiveness of multi-step methods in solving highly complex continuous control problems.
Toward Reliable Designs of Data-Driven Reinforcement Learning Tracking Control for Euler-Lagrange Systems
Our proposed new design takes advantage of two control design frameworks: a reinforcement learning based, data-driven approach to provide the needed adaptation and (sub)optimality, and a backstepping based approach to provide closed-loop system stability framework.
Reinforcement Learning Control of Robotic Knee with Human in the Loop by Flexible Policy Iteration
We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees such as stability and optimality at systems level.
Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven
In this paper time-driven learning refers to the machine learning method that updates parameters in a prediction model continuously as new data arrives.
Towards Expedited Impedance Tuning of a Robotic Prosthesis for Personalized Gait Assistance by Reinforcement Learning Control
Personalizing medical devices such as lower limb wearable robots is challenging.
