  How to extract as much learning signal from each trajectory data has been a
key problem in reinforcement learning (RL), where sample inefficiency has posed
serious challenges for practical applications. Recent works have shown that
using expressive policy function approximators and conditioning on future
trajectory information -- such as future states in hindsight experience replay
or returns-to-go in Decision Transformer (DT) -- enables efficient learning of
multi-task policies, where at times online RL is fully replaced by offline
behavioral cloning, e.g. sequence modeling. We demonstrate that all these
approaches are doing hindsight information matching (HIM) -- training policies
that can output the rest of trajectory that matches some statistics of future
state information. We present Generalized Decision Transformer (GDT) for
solving any HIM problem, and show how different choices for the feature
function and the anti-causal aggregator not only recover DT as a special case,
but also lead to novel Categorical DT (CDT) and Bi-directional DT (BDT) for
matching different statistics of the future. For evaluating CDT and BDT, we
define offline multi-task state-marginal matching (SMM) and imitation learning
(IL) as two generic HIM problems, propose a Wasserstein distance loss as a
metric for both, and empirically study them on MuJoCo continuous control
benchmarks. CDT, which simply replaces anti-causal summation with anti-causal
binning in DT, enables the first effective offline multi-task SMM algorithm
that generalizes well to unseen and even synthetic multi-modal state-feature
distributions. BDT, which uses an anti-causal second transformer as the
aggregator, can learn to model any statistics of the future and outperforms DT
variants in offline multi-task IL. Our generalized formulations from HIM and
GDT greatly expand the role of powerful sequence modeling architectures in
modern RL.

Machine Learning;Artificial Intelligence;Machine Learning