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Sep 22, 2023 · This paper evaluates more uncertainty criteria for efficient reinforcement learning. Our novel uncertainty criteria uses agent's multiple decisions at the same ...
Jun 22, 2024 · In reinforcement learning, an agent learns optimal actions for achieving a task by maximizing rewards in an environment. During learning, the agent decides ...
Sep 26, 2023 · This paper evaluates more uncertainty criteria for efficient reinforcement learning. Our novel uncertainty criteria uses agent's multiple decisions at the same ...
Nov 21, 2024 · This paper aims to provide researchers and practitioners with an evaluation of key influences which contribute to a successful generalization of ...
In reinforcement learning, an agent learns optimal actions for achieving a task by maximizing rewards in an environment. During learning, the agent decides ...
Apr 12, 2024 · In this work, we propose a State-Adversarial Markov Game (SAMG) and make the first attempt to investigate different solution concepts of MARL ...
Our descrip- tion is a viable and efficient implementation of the training of DQN with multiple heads. As illustrated in Figure 1b, we assume the network Q is.
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In reinforcement learning, an agent learns optimal actions for achieving a task by maximizing rewards in an environment. During learning, the agent decides its ...
The MAP serves effec- tively to generate better versions of the heads from Boot- strapped DQN, hence requiring only two of them (as op- posed to 10 or more).
In this paper, first we propose an entropy-guided adaptive co-construction method with and index using the entropy instead of the parameters for sufficiency.