Meet Text2Reward: A Data-Free Framework that Automates the Generation of Dense Reward Functions Based on Large Language Models

Reward shaping, which seeks to develop reward capabilities that extra successfully direct an agent in the direction of fascinating behaviors, continues to be a long-standing problem in reinforcement studying (RL). It is a time-consuming process that requires ability, could be sub-optimal, and is often accomplished manually by developing incentives based mostly on skilled instinct and heuristics. Reward shaping could also be addressed by way of inverse reinforcement studying (IRL) and desire studying. A reward mannequin could be taught utilizing preference-based suggestions or human examples. Both approaches nonetheless want vital labor or knowledge gathering, and the neural network-based reward fashions should be extra understandable and unable to generalize exterior the coaching knowledge’s domains. 

Researchers from The University of Hong Kong, Nanjing University, Carnegie Mellon University, Microsoft Research, and the University of Waterloo introduce the TEXT2REWARD framework for creating wealthy reward code based mostly on aim descriptions. TEXT2REWARD creates dense reward code (Figure 1 heart) based mostly on massive language fashions (LLMs), that are based mostly on a condensed, Pythonic description of the surroundings (Figure 1 left), given an RL goal (for instance, “push the chair to the marked position”). Then, an RL algorithm like PPO or SAC makes use of dense reward coding to coach a coverage (Figure 1 proper). In distinction to inverse RL, TEXT2REWARD produces symbolic rewards with good data-free interpretability. The authors’ free-form dense reward code, in distinction to current work that used LLMs to put in writing sparse reward code (the reward is non-zero solely when the episode ends) with hand-designed APIs, covers a wider vary of duties and might make use of confirmed coding frameworks (equivalent to NumPy operations over level clouds and agent positions). 

Finally, given the sensitivity of RL coaching and the ambiguity of language, the RL technique might fail to realize the intention or obtain it in methods that weren’t supposed. By making use of the discovered coverage in the actual world, getting person enter, and adjusting the reward as vital, TEXT2REWARD solves this difficulty. They carried out systematic research on two robotics manipulation benchmarks, MANISKILL2, METAWORLD, and two locomotion environments of MUJOCO. Policies skilled with their produced reward code obtain equal or better success charges and convergence speeds than the floor reality reward code meticulously calibrated by human specialists on 13 out of 17 manipulation duties. 

With a hit charge of over 94%, TEXT2REWARD learns 6 distinctive locomotor behaviors. Additionally, they present how the simulator-trained technique could also be utilized to a real Franka Panda robotic. Their strategy might iteratively enhance the success charge of discovered coverage from 0 to over 100% and eradicate job ambiguity with human enter in lower than three rounds. In conclusion, the experimental findings confirmed that TEXT2REWARD might present interpretable and generalizable dense reward code, enabling a human-in-the-loop pipeline and in depth RL job protection. They anticipate the outcomes will stimulate extra analysis into the interface between reinforcement studying and code creation.

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