proximal policy optimisation
PulseAugur coverage of proximal policy optimisation — every cluster mentioning proximal policy optimisation across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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New regularized RL algorithm enhances sim-to-real transfer for robots
A new reinforcement learning algorithm, Soft MellowMax Actor-Critic (SMM-AC), has been developed to improve the transfer of policies from simulation to real-world robots. This algorithm, based on the soft mellowmax valu…
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New RL-NAS method creates compact audio-visual speech enhancement models
Researchers have developed a new method for creating efficient audio-visual speech enhancement models using reinforcement learning-based neural architecture search (RL-NAS). This approach significantly reduces model siz…
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New RL trading system ViperQ integrates Auction Market Theory
Researchers have developed ViperQ, a reinforcement learning system designed for trading that incorporates principles from Auction Market Theory. This system utilizes a unique state representation derived from market mic…
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Soft Actor-Critic enhances heat pump control, reducing wear and improving efficiency
Researchers have developed a new reinforcement learning approach using Soft Actor-Critic (SAC) to improve the control of inverter-driven heat pumps. This method aims to reduce compressor wear by minimizing on-off cyclin…
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DocHRL framework uses reinforcement learning for cost-optimized document classification
Researchers have developed DocHRL, a novel hierarchical reinforcement learning framework designed to optimize document classification costs. This system adaptively selects the most efficient classification policy for ea…
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Robotics method uses RL to handle missing sensor data
Researchers have developed RL4IL, a novel reinforcement learning approach designed to enhance multimodal imitation learning in robotics, particularly when sensor data is missing. This method utilizes reinforcement learn…
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New RL algorithm optimizes stock trade execution
Researchers have developed a new reinforcement learning algorithm called TT-DAC-PS for optimizing stock trade execution. This deterministic actor-critic architecture incorporates several advanced techniques, including t…