Actor-critic algorithm
PulseAugur coverage of Actor-critic algorithm — every cluster mentioning Actor-critic algorithm across labs, papers, and developer communities, ranked by signal.
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Survey maps reinforcement learning implementations on FPGAs and ICs
This survey paper reviews over 100 studies on the implementation of reinforcement learning (RL) algorithms on field-programmable gate arrays (FPGAs) and integrated circuits (ICs). It categorizes research by RL algorithm…
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Foundation models boost MARL efficiency for wireless networks
Researchers have developed a novel approach using foundation models to enhance the efficiency of multi-agent reinforcement learning (MARL) for wireless random access network optimization. This new method, detailed in a …
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SEPAL framework enhances LLM collaboration for improved question answering
Researchers have developed SEPAL, a novel framework for enhancing Large Language Model (LLM) collaboration in question answering. SEPAL employs three distinct, privately-trained Actor-Critic teams for reasoning, evidenc…
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AI framework optimizes satellite scheduling with reinforcement learning
Researchers have developed a novel framework for optimizing the scheduling of heterogeneous agile Earth observation satellites. This framework combines indirect encoding with decoder-based evaluation to create a unified…
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New method optimizes foundation model fine-tuning under resource limits
Researchers have developed a novel approach to optimize the continual fine-tuning of foundation models on resource-constrained devices. By formulating the problem as a constrained Markov Decision Process, their method, …
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New method optimizes foundation model fine-tuning under compute limits
Researchers have developed a reinforcement learning-based method, utilizing an actor-critic algorithm, to optimize the continual fine-tuning of foundation models on resource-limited devices. This approach addresses the …
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New AI Framework Optimizes Decision-Making in Complex Environments
Researchers have developed a new method for creating performance-driven environment abstractions in large Markov decision processes. This approach focuses on optimizing decision quality by aggregating states and enforci…
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New actor-critic algorithm optimizes dynamic risk management
Researchers have developed a new actor-critic algorithm designed to optimize dynamic risk management in stochastic policies. This novel approach bypasses the need for transition perturbation in policy updates and utiliz…