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pro vyhledávání: '"Wiltzer, Harley"'
When decisions are made at high frequency, traditional reinforcement learning (RL) methods struggle to accurately estimate action values. In turn, their performance is inconsistent and often poor. Whether the performance of distributional RL (DRL) ag
Externí odkaz:
http://arxiv.org/abs/2410.11022
In reinforcement learning (RL), the consideration of multivariate reward signals has led to fundamental advancements in multi-objective decision-making, transfer learning, and representation learning. This work introduces the first oracle-free and co
Externí odkaz:
http://arxiv.org/abs/2409.00328
Autor:
Wiltzer, Harley, Farebrother, Jesse, Gretton, Arthur, Tang, Yunhao, Barreto, André, Dabney, Will, Bellemare, Marc G., Rowland, Mark
This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process. Analogous to how the successor representation (SR) describes the expected
Externí odkaz:
http://arxiv.org/abs/2402.08530
Deep reinforcement learning agents for continuous control are known to exhibit significant instability in their performance over time. In this work, we provide a fresh perspective on these behaviors by studying the return landscape: the mapping betwe
Externí odkaz:
http://arxiv.org/abs/2309.14597
Continuous-time reinforcement learning offers an appealing formalism for describing control problems in which the passage of time is not naturally divided into discrete increments. Here we consider the problem of predicting the distribution of return
Externí odkaz:
http://arxiv.org/abs/2205.12184