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 …

Transformers have significantly impacted domains like natural language processing,
computer vision, and robotics, where they improve performance compared to other neural …

Reinforcement learning (RL) algorithms face two distinct challenges: learning effective
representations of past and present observations, and determining how actions influence …

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 study counterfactual identifiability in causal models with bijective generation
mechanisms (BGM), a class that generalizes several widely-used causal models in the …

The number of states in a dynamic process is exponential in the number of objects, making
reinforcement learning (RL) difficult in complex, multi-object domains. For agents to scale to …

Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many
issues that make it difficult for deep reinforcement learning (RL) agents to learn optimal …

To make reinforcement learning more sample efficient, we need better credit assignment
methods that measure an action's influence on future rewards. Building upon Hindsight …

In popular media, there is often a connection drawn between the advent of awareness in
artificial agents and those same agents simultaneously achieving human or superhuman …