개념 공부/Reinforcement learning4 Dream to Control : Learning Behaviors by Latent Imagination https://arxiv.org/abs/1912.01603 Dream to Control: Learning Behaviors by Latent Imagination Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors arxiv.org Introduction 이 논문에서는 model-based RL 알고리.. 2024. 2. 1. Addressing Function Approximation Error in Actor-Critic Methods (TD3) https://arxiv.org/abs/1802.09477 Addressing Function Approximation Error in Actor-Critic Methods In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel arxiv.org Concepts "Twin Delayed Deep Determ.. 2024. 1. 31. Asynchronous Methods for Deep Reinforcement Learning (A3C) https://arxiv.org/abs/1602.01783 Asynchronous Methods for Deep Reinforcement Learning We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning arxiv.org Concepts 본 논문에서는 기존 off-policy method들의 memor.. 2024. 1. 30. Soft Actor-Critic (SAC) https://arxiv.org/abs/1801.01290 Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergenc arxiv.org .. 2024. 1. 27. 이전 1 다음
