Attention mechanism dynamically groups task knowledge at state granularity in multi-task DRL to enable positive transfer and avoid negative transfer, matching or exceeding prior methods with fewer parameters.
In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 (2017)
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Temporal abstraction functions as a low-pass filter on transition dynamics to lower the effective rank of successor representations while bounding value function error in forward-backward learning.
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Attentive Multi-Task Deep Reinforcement Learning
Attention mechanism dynamically groups task knowledge at state granularity in multi-task DRL to enable positive transfer and avoid negative transfer, matching or exceeding prior methods with fewer parameters.
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Spectral Alignment in Forward-Backward Representations via Temporal Abstraction
Temporal abstraction functions as a low-pass filter on transition dynamics to lower the effective rank of successor representations while bounding value function error in forward-backward learning.