Rewards

1. Competitive experience replay
2. Episodic Curiosity through Reachability
3. Diversity is All You Need: Learning Skills without a Reward Function

Value functions (3)

1. Value Propagation Networks
2. Recall Traces: Backtracking Models for Efficient Reinforcement Learning
3. Soft Q-Learning with Mutual-Information Regularization

Imitation learning, RL from observational data (4)

1. Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
2. Learning what you can do before doing anything
3. Intrinsically motivated reinforcement learning forhuman-robot interaction in the real-world
4. Learning driving styles for autonomous vehicles from demonstration

Multi-Armed Bandits

1. The K-armed Dueling Bandits Problem
2. Restless bandits: indexability and computation of Whittle index
3. Risk–Aversion in Multi–armed Bandits

Reinforcement Learning for Games (5)

1. Human-level control through deep reinforcement learning
2. Deep Reinforcement Learning with Double Q-learning
3. Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
4. The Predictron: End-To-End Learning and Planning
5. ELF OpenGo: an analysis and open reimplementation of AlphaZero
6. Solving the Rubik's Cube with Approximate Policy Iteration

RL for Robotics

1. Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
2. Sim-to-Real Robot Learning from Pixels withProgressive Nets

Exploration / Exploitation (3)

1. Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
2. Information-Directed Exploration for Deep Reinforcement Learning
3. Off-Policy Deep Reinforcement Learning without Exploration

Actor-Critic

1. Reward Constrained Policy Optimization
2. Soft Actor-Critic:Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor