Robotic Knee Parameter Tuning Using Approximate Policy Iteration

2019 ◽  
pp. 554-563
Author(s):  
Xiang Gao ◽  
Yue Wen ◽  
Minhan Li ◽  
Jennie Si ◽  
He Huang
Keyword(s):  
Parameter Tuning ◽  
Policy Iteration ◽  
Approximate Policy Iteration

Data-based approximate policy iteration for affine nonlinear continuous-time optimal control design

Automatica ◽  
2014 ◽  
Vol 50 (12) ◽  
pp. 3281-3290 ◽  
Author(s):  
Biao Luo ◽  
Huai-Ning Wu ◽  
Tingwen Huang ◽  
Derong Liu
Keyword(s):  
Optimal Control ◽  
Continuous Time ◽  
Control Design ◽  
Policy Iteration ◽  
Time Optimal Control ◽  
Time Optimal ◽  
Approximate Policy Iteration

Multi-agent reinforcement learning using ordinal action selection and approximate policy iteration

2016 ◽  
Vol 14 (06) ◽  
pp. 1650053
Author(s):  
Daxue Liu ◽  
Jun Wu ◽  
Xin Xu
Keyword(s):  
Reinforcement Learning ◽  
Single Agent ◽  
Action Selection ◽  
Policy Iteration ◽  
Common Interest ◽  
Policy Space ◽  
Markov Games ◽  
Approximate Policy Iteration ◽  
Multi Agent ◽  
Agent Coordination

Multi-agent reinforcement learning (MARL) provides a useful and flexible framework for multi-agent coordination in uncertain dynamic environments. However, the generalization ability and scalability of algorithms to large problem sizes, already problematic in single-agent RL, is an even more formidable obstacle in MARL applications. In this paper, a new MARL method based on ordinal action selection and approximate policy iteration called OAPI (Ordinal Approximate Policy Iteration), is presented to address the scalability issue of MARL algorithms in common-interest Markov Games. In OAPI, an ordinal action selection and learning strategy is integrated with distributed approximate policy iteration not only to simplify the policy space and eliminate the conflicts in multi-agent coordination, but also to realize the approximation of near-optimal policies for Markov Games with large state spaces. Based on the simplified policy space using ordinal action selection, the OAPI algorithm implements distributed approximate policy iteration utilizing online least-squares policy iteration (LSPI). This resulted in multi-agent coordination with good convergence properties with reduced computational complexity. The simulation results of a coordinated multi-robot navigation task illustrate the feasibility and effectiveness of the proposed approach.

scholarly journals Rollout sampling approximate policy iteration

2008 ◽  
Vol 72 (3) ◽  
pp. 157-171 ◽  
Author(s):  
Christos Dimitrakakis ◽  
Michail G. Lagoudakis
Keyword(s):  
Policy Iteration ◽  
Approximate Policy Iteration

Hierarchical Approximate Policy Iteration With Binary-Tree State Space Decomposition

2011 ◽  
Vol 22 (12) ◽  
pp. 1863-1877 ◽  
Author(s):  
Xin Xu ◽  
Chunming Liu ◽  
S. X. Yang ◽  
Dewen Hu
Keyword(s):  
State Space ◽  
Binary Tree ◽  
Policy Iteration ◽  
Space Decomposition ◽  
Approximate Policy Iteration

Simulation-Based Approximate Policy Iteration with Generalized Logistic Functions

2015 ◽  
Vol 27 (3) ◽  
pp. 579-595 ◽  
Author(s):  
Antoine Sauré ◽  
Jonathan Patrick ◽  
Martin L. Puterman
Keyword(s):  
Policy Iteration ◽  
Simulation Based ◽  
Approximate Policy Iteration ◽  
Logistic Functions
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