분류 전체보기 (40) 썸네일형 리스트형 [2018.02] Learning Exploration Strategies with Meta-Reinforcement Learning In this work, we explore how prior tasks can inform an agent about how to explore effectively in new situations. We introduce a novel gradient-based fast adaptation algorithm – model agnostic exploration with structured noise (MAESN) – to learn exploration strategies from prior experience. The prior experience is used both to initialize a policy and to acquire a latent exploration space that can.. [2020.05] MOReL: Model-Based Offline Reinforcement Learning In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based solely on a dataset of historical interactions with the environment. The ability to train RL policies offline would greatly expand where RL can be applied, its data efficiency, and its experimental velocity. Prior work in offline RL has been confined almost exclusively to model-free RL approaches. In thi.. [2018.04] Distributed Distributional Deterministic Policy Gradients This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting. We combine this within a distributed framework for off-policy learning in order to develop what we call the Distributed Distributional Deep Deterministic Policy Gradient algorithm, D4PG. We also combine this technique with a number of additional, simple impro.. [2017.12] Learning Multi-level Hierarchies with Hindsight Hierarchical agents have the potential to solve sequential decision-making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions. In order to realize this potential of faster learning, hierarchical agents need to be able to learn their multiple levels of p.. [2019.07] Dynamics-Aware Unsupervised Discovery of Skills Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms to generate a large variety of behaviors and solve diverse tasks. However, learning an accurate model for complex dynamical systems is difficult, and even then, the model might not generalize well outside the distribut.. [2018.02] Diversity is all you need: Learning skills without a reward function Intelligent creatures can explore their environments and learn useful skills without supervision. In this paper, we propose “Diversity is All You Need”(DIAYN), a method for learning useful skills without a reward function. Our proposed method learns skills by maximizing an information-theoretic objective using a maximum entropy policy. On a variety of simulated robotic tasks, we show that this s.. [2019.03] Model-Based Reinforcement Learning for Atari Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction – substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works an.. [2020.05] Planning to explore via self-supervised world models Reinforcement learning allows solving complex tasks, however, the learning tends to be task-specific and the sample efficiency remains a challenge. We present Plan2Explore, a self-supervised reinforcement learning agent that tackles both these challenges through a new approach to self-supervised exploration and fast adaptation to new tasks, which need not be known during exploration. During expl.. 이전 1 2 3 4 5 다음
