Reinforcement Learning
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks.
OFCOURSE: A Multi-Agent Reinforcement Learning Environment for Order Fulfillment
In particular, we model the integrated problem as a Markov game, wherein a team of agents learns a joint policy via interacting with a simulated environment.
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.
VOCE: Variational Optimization with Conservative Estimation for Offline Safe Reinforcement Learning
This arrangement is particularly important in scenarios with high sampling costs and potential dangers, such as autonomous driving and robotics.
Probabilistic Inference in Reinforcement Learning Done Right Jean T arbouriech Google DeepMind
A popular perspective in Reinforcement learning (RL) casts the problem as probabilistic inference on a graphical model of the Markov decision process (MDP). The core object of study is the probability of each state-action pair being visited under the optimal policy.
Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration
Source code is available at https://sites.google.com/view/rl-vcse .
