Reinforcement Learning
Credit Assignment For Collective Multiagent RL With Global Rewards
Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Neural Information Processing Systems http://nips.cc/
Optimistic Exploration in Reinforcement Learning Using Symbolic Model Estimates
A popular trend in recent years is using symbolic planning models with reinforcement learning (RL) algorithms.
Online Control with Adversarial Disturbance for Continuous-time Linear Systems
A major challenge in robotics is to deploy simulated controllers into real-world.
Reward learning from human preferences and demonstrations in Atari
Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei
Neural Information Processing Systems http://nips.cc/
From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces Peter Shaw
Much of the previous work towards digital agents for graphical user interfaces (GUIs) has relied on text-based representations (derived from HTML or other structured data sources), which are not always readily available.
Online Robust Policy Learning in the Presence of Unknown Adversaries
Aaron Havens, Zhanhong Jiang, Soumik Sarkar
Lemma 2. Let Assumption 1 hold.
Evolution-Guided Policy Gradient in Reinforcement Learning
Temporal Difference methods inRL use bootstrapping to address this issue but often struggle when the time horizons are long and the reward is sparse.