Meet VLM-CaR (code as a reward): a new machine learning framework that powers reinforcement learning with vision-language models

Researchers at Google DeepMind have collaborated with Mila and McGill University to define appropriate reward functions to address the challenge of efficiently training reinforcement learning (RL) agents. The reinforcement learning method uses a reward system for achieving desired behaviors and punishing unwanted ones. Therefore, designing effective reward functions is crucial for RL agents to learn efficiently, but often requires significant effort on the part of environment designers. The paper proposes to leverage vision and language models (VLM) to automate the reward function generation process.

Existing models defining the reward function for RL agents have been a manual and laborious process, often requiring domain expertise. The paper presents a framework called Code as Reward (VLM-CaR), which uses pre-trained VLMs to generate dense reward functions for RL agents automatically. Unlike directly querying rewards to VLMs, which is computationally expensive and unreliable, VLM-CaR generates reward functions through code generation, which significantly reduces the computational burden. Using this framework, the researchers attempted to provide accurate rewards that are interpretable and can be derived from visual information.

VLM-CaR operates in three stages: program generation, program verification, and RL training. In the first stage, pretrained VLMs are asked to describe tasks and subtasks based on initial and target images of an environment. The generated descriptions are then used to produce executable computer programs for each subtask. The generated programs are verified for correctness using expert and random trajectories. After the verification step, the programs act as reward functions to train the RL agents. Using the generated reward function, VLM-CaR is trained for RL policies and enables efficient training even in environments with scarce or unavailable rewards.

In conclusion, the proposed method addresses the problem of manually defining reward functions by providing a systematic framework for generating interpretable rewards from visual observations. VLM-CaR demonstrates the potential to significantly improve the training efficiency and performance of RL agents in various environments.

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