Reinforcement learning generalization using state aggregation with a maze-solving problem

2012 ◽  
Author(s):  
Mohamed K. Gunady ◽  
Walid Gomaa
Keyword(s):  
Reinforcement Learning ◽  
State Aggregation ◽  
Learning Generalization

scholarly journals Effective Experiences Collection and State Aggregation in Reinforcement Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 120917-120928
Author(s):  
Tao Zhang ◽  
Ying Liu ◽  
Yu-Jen Chen ◽  
Kao-Shing Hwang
Keyword(s):  
Reinforcement Learning ◽  
State Aggregation

FUZZY STATE AGGREGATION AND POLICY HILL CLIMBING FOR STOCHASTIC ENVIRONMENTS

2006 ◽  
Vol 06 (03) ◽  
pp. 413-428 ◽  
Author(s):  
DEAN C. WARDELL ◽  
GILBERT L. PETERSON
Keyword(s):  
Reinforcement Learning ◽  
Function Approximation ◽  
Hill Climbing ◽  
Individual Agent ◽  
Q Learning ◽  
Stochastic Environments ◽  
State Aggregation ◽  
Multi Agent ◽  
Fuzzy State ◽  
Better Than

Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the operating environment changes. Additionally, by applying reinforcement learning to multiple cooperative software agents (a multi-agent system) not only allows each individual agent to learn from its own experience, but also opens up the opportunity for the individual agents to learn from the other agents in the system, thus accelerating the rate of learning. This research presents the novel use of fuzzy state aggregation, as the means of function approximation, combined with the fastest policy hill climbing methods of Win or Lose Fast (WoLF) and policy-dynamics based WoLF (PD-WoLF). The combination of fast policy hill climbing and fuzzy state aggregation function approximation is tested in two stochastic environments: Tileworld and the simulated robot soccer domain, RoboCup. The Tileworld results demonstrate that a single agent using the combination of FSA and PHC learns quicker and performs better than combined fuzzy state aggregation and Q-learning reinforcement learning alone. Results from the multi-agent RoboCup domain again illustrate that the policy hill climbing algorithms perform better than Q-learning alone in a multi-agent environment. The learning is further enhanced by allowing the agents to share their experience through a weighted strategy sharing.

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