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TempoRL - Learning When to Act
Getting the best out of RL by learning when to act.
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Theory-Inspired Parameter Control Benchmarks for DAC
Accompanying blog post for our GECCO'22 paper
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The Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
In depth discussion on importance of HPO for MBRL. Redicrects to the post on Berkeley AI Research.
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AutoRL: AutoML for RL
Reinforcement learning (RL) has shown impressive results in a variety of applications. Well known examples include game and video game playing, robotics and, recently, “Autonomous navigation of stratospheric balloons”. A lot of the successes came about by combining the expressiveness of deep learning with the power of RL. Already on their own though, both frameworks […]
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Learning Step-Size Adaptation in CMA-ES
In a Nutshell In CMA-ES, the step size controls how fast or slow a population traverses through a search space. Large steps allow you to quickly skip over uninteresting areas (exploration), whereas small steps allow a more focused traversal of interesting areas (exploitation). Handcrafted heuristics usually trade off small and large steps given some measure […]