https://www.sciencedirect.com/science/article/pii/S0004370221000862?fbclid=IwAR00HAZ1VgULd647jwVdXSCG58RlcWsC9GpPUimy0JvEgGNLYeKNI-_UWWc Reward is enough In this article we hypothesise that intelligence, and its associated abilities, can be understood as subserving the maximisation of reward. Accordingl… www.sciencedirect.com 1. 근본적인 의문: 어떻게 자연에서의 에이전트(동물), 사람은 똑똑하게 행동하는가? 에 대답하기 위한 답변으로 “모든것이 g..

https://openreview.net/pdf?id=nIAxjsniDzg We train over 250’000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for the training of on-policy deep actor-critic RL agents. 0. Deep Actor-Critic Methods Policy-Based 계열의 Deep Actor-Critic Method들은 Hopper, Humanoid와 같은 D4RL 벤치마크에 있는 continuous task에서 아주 좋은 성능을 냈다. REINFORCE, TR..