Direct alignment from preferences (DAP) methods, such as DPO, have recently emerged as
efficient alternatives to reinforcement learning from human feedback (RLHF), that do not …

Exploring rich environments and evaluating one's actions without prior knowledge is
immensely challenging. In this paper, we propose Motif, a general method to interface such …

Developing agents that can execute multiple skills by learning from pre-collected datasets is
an important problem in robotics, where online interaction with the environment is extremely …

It has been a long-standing dream to design artificial agents that explore their environment
efficiently via intrinsic motivation, similar to how children perform curious free play. Despite …

To obtain a near-optimal policy with fewer interactions in Reinforcement Learning (RL), a
promising approach involves the combination of offline RL, which enhances sample …

Building generally capable agents is a grand challenge for deep reinforcement learning
(RL). To approach this challenge practically, we outline two key desiderata: 1) to facilitate …

State of the art reinforcement learning has enabled training agents on tasks of ever
increasing complexity. However, the current paradigm tends to favor training agents from …

The success of transformer models trained with a language modeling objective brings a
promising opportunity to the reinforcement learning framework. Decision Transformer is a …

Behavioral cloning (BC) provides a straightforward solution to offline RL by mimicking offline
trajectories via supervised learning. Recent advances (Chen et al., 2021; Janner et al …

Reinforcement learning solely from an agent's self-generated data is often believed to be
infeasible for learning on real robots, due to the amount of data needed. However, if done …