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The document presents a novel algorithm called LifeLong Incremental Reinforcement Learning (LLIRL) designed for efficient adaptation to dynamic environments in reinforcement learning. It employs an infinite mixture of Bayesian models and the Chinese restaurant process to incrementally build and update environment models, allowing for effective learning from past experiences. Simulation experiments demonstrate that LLIRL outperforms existing methods, enabling intelligent agents to adapt to various dynamic sc

Author

ananta.jobs

Language

EN