Reinforcement learning (RL) allows an agent to solve sequential decision-making problems
by interacting with an environment in a trial-and-error fashion. When these environments are …

Transformer architectures have facilitated the development of large-scale and general-
purpose sequence models for prediction tasks in natural language processing and computer …

We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence
modeling problem. This allows us to draw upon the simplicity and scalability of the …

Atari games have been a long-standing benchmark in the reinforcement learning (RL)
community for the past decade. This benchmark was proposed to test general competency …

Due to its limited intelligence and abilities, machine learning is currently unable to handle
various situations thus cannot completely replace humans in real-world applications …

Many problems in RL, such as meta-RL, robust RL, generalization in RL, and temporal credit
assignment, can be cast as POMDPs. In theory, simply augmenting model-free RL with …

We propose RUDDER, a novel reinforcement learning approach for delayed rewards in
finite Markov decision processes (MDPs). In MDPs the Q-values are equal to the expected …

Animals frequently make decisions based on expectations of future reward (" values").
Values are updated by ongoing experience: places and choices that result in reward are …

Credit assignment in reinforcement learning is the problem of measuring an action's
influence on future rewards. In particular, this requires separating skill from luck, ie …

Reinforcement Learning from Human Feedback (RLHF) has been credited as the key
advance that has allowed Large Language Models (LLMs) to effectively follow instructions …