  Designing reward functions is a longstanding challenge in reinforcement
learning (RL); it requires specialized knowledge or domain data, leading to
high costs for development. To address this, we introduce Text2Reward, a
data-free framework that automates the generation and shaping of dense reward
functions based on large language models (LLMs). Given a goal described in
natural language, Text2Reward generates shaped dense reward functions as an
executable program grounded in a compact representation of the environment.
Unlike inverse RL and recent work that uses LLMs to write sparse reward codes
or unshaped dense rewards with a constant function across timesteps,
Text2Reward produces interpretable, free-form dense reward codes that cover a
wide range of tasks, utilize existing packages, and allow iterative refinement
with human feedback. We evaluate Text2Reward on two robotic manipulation
benchmarks (ManiSkill2, MetaWorld) and two locomotion environments of MuJoCo.
On 13 of the 17 manipulation tasks, policies trained with generated reward
codes achieve similar or better task success rates and convergence speed than
expert-written reward codes. For locomotion tasks, our method learns six novel
locomotion behaviors with a success rate exceeding 94%. Furthermore, we show
that the policies trained in the simulator with our method can be deployed in
the real world. Finally, Text2Reward further improves the policies by refining
their reward functions with human feedback. Video results are available at
https://text-to-reward.github.io/ .
