We’ve found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance. This exploration method is simple to implement and very rarely decreases performance, so it’s worth trying on any problem.
As TrickBot evolves, we examine version 24, which heavily targets Nordic financial institutions, and we take a close look at the Dyre–TrickBot connection.
As TrickBot evolves, we examine version 24, which heavily targets Nordic financial institutions, and we take a close look at the Dyre–TrickBot connection.
As TrickBot evolves, we examine version 24, which heavily targets Nordic financial institutions, and we take a close look at the Dyre–TrickBot connection.
We’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better than state-of-the-art approaches while being much simpler to implement and tune. PPO has become the default reinforcement learning algorithm at OpenAI because of its ease of use and good performance.