The paper "Diversity is All You Need: Learning Skills without a Reward Function" by Benjamin Eysenbach proposes a method called "Diversity is All You Need" (DIAYN) for learning useful skills in the absence of a reward function. DIAYN aims to maximize an information-theoretic objective using a maximum entropy policy, enabling the emergence of diverse skills such as walking and jumping in simulated robotic tasks. The method is evaluated on various benchmark environments, where it successfully learns skills that solve the tasks despite never receiving the true task reward. The paper also demonstrates how these learned skills can be used for hierarchical reinforcement learning, imitation learning, and policy initialization, showing their effectiveness in improving task performance and efficiency. The key contributions include a novel objective function, the ability to learn diverse skills, and the application of these skills in downstream tasks.The paper "Diversity is All You Need: Learning Skills without a Reward Function" by Benjamin Eysenbach proposes a method called "Diversity is All You Need" (DIAYN) for learning useful skills in the absence of a reward function. DIAYN aims to maximize an information-theoretic objective using a maximum entropy policy, enabling the emergence of diverse skills such as walking and jumping in simulated robotic tasks. The method is evaluated on various benchmark environments, where it successfully learns skills that solve the tasks despite never receiving the true task reward. The paper also demonstrates how these learned skills can be used for hierarchical reinforcement learning, imitation learning, and policy initialization, showing their effectiveness in improving task performance and efficiency. The key contributions include a novel objective function, the ability to learn diverse skills, and the application of these skills in downstream tasks.