Proximal Policy Optimization (PPO), initially designed for continuous control tasks, is widely used in reinforcement learning (RL) applications, like fine-tuning generative models. However, PPO's effectiveness is based on a series of heuristics for stable convergence, like value networks and clipping, adding complexities in its implementation.
Adapting PPO to optimize complex modern generative models with billions of…
Reinforcement Learning (RL) expands beyond its origins in gaming and finds innovative applications across various industries such as finance, healthcare, robotics, autonomous vehicles, and smart infrastructure.
In finance, RL algorithms are reinventing investment strategies and risk management by making sequential decisions, observing market conditions, and adjusting strategies based on rewards. Despite their potential, these algorithms struggle…
Researchers from the University of Oxford and University College London have developed Craftax, a reinforcement learning (RL) benchmark that unifies effective parallelization, compilation, and the removal of CPU to GPU transfer in RL experiments. This research seeks to address the limitations educators face in using tools such as MiniHack and Crafter due to their prolonged…