Deep Learning/강화학습 (18) 썸네일형 리스트형 [2017.06] Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as the number of agents grows. We then present an adaptation of actor-critic methods that considers .. [2018.05] Learning Attentional Communication for Multi-Agent Cooperation Communication could potentially be an effective way for multi-agent cooperation. However, information sharing among all agents or in predefined communication architectures that existing methods adopt can be problematic. When there is a large number of agents, agents cannot differentiate valuable information that helps cooperative decision-making from globally shared information. Therefore, commu.. [2018.03] Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning This work proposes to learn how to quickly and effectively adapt online to new tasks. To enable sample-efficient learning, we consider learning online adaptation in the context of model-based reinforcement learning. Our approach uses meta-learning to train a dynamics model prior such that, when combined with recent data, this prior can be rapidly adapted to the local context. URL: arxiv.org/abs/.. [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.. 이전 1 2 3 다음
