  The goal of continuous control is to synthesize desired behaviors. In
reinforcement learning (RL)-driven approaches, this is often accomplished
through careful task reward engineering for efficient exploration and running
an off-the-shelf RL algorithm. While reward maximization is at the core of RL,
reward engineering is not the only -- sometimes nor the easiest -- way for
specifying complex behaviors. In this paper, we introduce \braxlines, a toolkit
for fast and interactive RL-driven behavior generation beyond simple reward
maximization that includes Composer, a programmatic API for generating
continuous control environments, and set of stable and well-tested baselines
for two families of algorithms -- mutual information maximization (MiMax) and
divergence minimization (DMin) -- supporting unsupervised skill learning and
distribution sketching as other modes of behavior specification. In addition,
we discuss how to standardize metrics for evaluating these algorithms, which
can no longer rely on simple reward maximization. Our implementations build on
a hardware-accelerated Brax simulator in Jax with minimal modifications,
enabling behavior synthesis within minutes of training. We hope Braxlines can
serve as an interactive toolkit for rapid creation and testing of environments
and behaviors, empowering explosions of future benchmark designs and new modes
of RL-driven behavior generation and their algorithmic research.
