Anna KoopRecent experience can be more relevant than past experience. Most machine learning is oriented toward convergence to a single solution, which implicitly weights all experience equally. We show that it may be advantageous to overweight recent experience even in static worlds. Tracking and forgetting both give precedence to recent experience. These techniques can result in more accurate predictions if the environment is temporally coherent and approximation resources are limited. Attached Files: |
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