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保存一些自己看过的论文

Reinforcement Learning: An Introduction
Spinning Up Documentation

DQN: Human-level control through deep reinforcement learning
Playing Atari with Deep Reinforcement Learning
DPG: Deterministic policy gradient algorithms
DDPG: Continuous control with deep reinforcement learning
PG: Policy Gradient Methods for Reinforcement Learning with Function Approximation
A3C: Asynchronous methods for deep reinforcement learning
TRPO: Approximately optimal approximate reinforcement learning(前序工作)
Trust region policy optimization
PPO: Proximal policy optimization algorithms

HAM: Reinforcement Learning with Hierarchies of Machines
MAXQ: The MAXQ Method for Hierarchical Reinforcement Learning
Option: Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
OC: The Option-Critic Architecture
A2OC: When Waiting is not an Option Learning Options with a Deliberation Cost
Feudal: Feudal Reinforcement Learning
FuNs: FeUdal Networks for Hierarchical Reinforcement Learning
h-DQN: Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
H-DRLN: A Deep Hierarchical Approach to Lifelong Learning in Minecraft
UVFA: Universal Value Function Approximators
HER: Hindsight Experience Replay
HAC: Learning Multi-Level Hierachies with Hindsight
HIRO: Data-Efficient Hierarchical Reinforcement Learning
SC: Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining
DSC: Option Discovery Using Deep Skill Chaining
CEICP: Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
DADS: Dynamic-Aware Unsupervised Discovery Skills
DIAYN: Diversity Is All You Need: Learning Skills Without A Reward Function
VIM: Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning