Researchers from Google DeepMind explore leveraging off-the-shelf vision-language models, specifically CLIP, to derive rewards for training diverse language goals for reinforcement learning agents. The study demonstrates that larger VLMs lead to more accurate rewards and more capable agents, offering potential for training versatile RL agents without environment-specific finetuning in visual domains.
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Google DeepMind Researchers Utilize Vision-Language Models to Transform Reward Generation in Reinforcement Learning for Generalist Agents
Reinforcement learning (RL) agents represent the pinnacle of AI, adapting and evolving through trial and error to optimize decision-making. Developing generalist RL agents capable of diverse tasks in complex environments is challenging, but researchers are exploring solutions.
Research Overview
Researchers from Google DeepMind are investigating the use of off-the-shelf vision-language models (VLMs), such as the CLIP family, to derive rewards for training RL agents with diverse language goals. The study demonstrates that larger VLMs lead to more accurate rewards, enhancing the capabilities of RL agents. By converting the reward function into a binary form through probability thresholding, the research addresses the challenge of creating versatile RL agents capable of diverse goals in complex environments.
The study utilizes contrastive VLMs like CLIP to generate text-based reward models for reinforcement learning agents, aiming to streamline RL agent training. The approach highlights off-the-shelf VLMs, specifically CLIP, as reward sources for RL agents, demonstrating their potential for training versatile RL agents in diverse language goals within visual environments.
Key Findings
- The study proposes a method to obtain sparse binary rewards for reinforcement learning agents using pre-trained CLIP embeddings for visual achievement of language goals.
- Off-the-shelf VLMs, such as CLIP, can be rewarded sources without environment-specific finetuning.
- Larger VLMs lead to more accurate rewards and more capable RL agents.
- Maximizing VLM rewards enhances ground truth rewards, and scaling VLM size positively impacts performance.
- The study examines the role of prompt engineering in VLM reward performance.
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