Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …

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 …

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 …

Normative models of synaptic plasticity use computational rationales to arrive at predictions
of behavioral and network-level adaptive phenomena. In recent years, there has been an …

Recent human behavioral and neuroimaging results suggest that people are selective in
when they encode and retrieve episodic memories. To explain these findings, we trained a …

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 …

Recently, sequence learning methods have been applied to the problem of off-policy
Reinforcement Learning, including the seminal work on Decision Transformers, which …

The Credit Assignment Problem (CAP) refers to the longstanding challenge of
Reinforcement Learning (RL) agents to associate actions with their long-term …

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 …