This AI article has breakthroughs: How language models adapt to offline reinforcement learning with ‘LaMo’ dance steps and sparse learning

Researchers present Language Models for Motion Control (LaMo), a framework that uses large language models (LLM) for offline reinforcement learning. It leverages pretrained LLMs to enhance RL policy learning, employing LLM-initialized decision transformers (DTs) and LoRA tuning. LaMo outperforms existing methods on sparse reward tasks and bridges the gap between value-based offline RL and decision transformers on dense reward tasks, particularly excelling in scenarios with limited data samples.

The current research explores the synergy between transformers, particularly DT, and LLMs for decision making in RL tasks. LLMs have previously shown promise in high-level task decomposition and policy generation. LaMo is a novel framework that leverages pre-trained LLMs for motion control tasks, outperforming existing methods in sparse reward scenarios and bridging the gap between value-based offline RL and decision transformers in dense reward tasks. . It builds on previous work such as Wiki-RL, with the goal of better leveraging pre-trained LMs for offline RL.

The approach reformulates RL as a conditional sequence modeling problem. LaMo surpasses existing methods by combining LLM with DT and introduces innovations such as LoRA fine-tuning, nonlinear MLP projections, and auxiliary language loss. It excels in tasks with sparse rewards and reduces the performance gap between value-based and DT-based methods in scenarios with dense rewards.

The LaMo framework for offline reinforcement learning incorporates pre-trained LM and DT. It enhances representation learning with multilayer perceptrons and employs LoRA fine-tuning with an auxiliary language prediction loss to effectively combine knowledge from LMs. Extensive experiments on various tasks and environments evaluate performance under different data ratios, comparing it to robust RL baselines such as CQL, IQL, TD3BC, BC, DT, and Wiki-RL.

The LaMo framework excels on sparse and reward-dense tasks, outperforming Decision Transformer and Wiki-RL. Outperforms several strong RL baselines, including CQL, IQL, TD3BC, BC, and DT, while avoiding overfitting: LaMo’s robust learning ability, especially with limited data, benefits from the inductive bias of pretrained LMs . D4RL benchmark evaluation and extensive ablation studies confirm the effectiveness of each component within the framework.

The study needs an in-depth exploration of higher-level representation learning techniques to improve the overall generalization of the fit. Computational limitations limit the examination of alternative approaches such as co-training. The impact of different pretraining qualities of LMs still needs to be addressed beyond comparing GPT-2, early-stopped pretrained, and randomly pretrained models. Specific numerical results and performance metrics are required to substantiate claims of cutting-edge performance and basic superiority.

In conclusion, the LaMo framework uses pre-trained LMs for motion control in offline RL, achieving superior performance in low-reward tasks compared to CQL, IQL, TD3BC, and DT. Reduces the performance gap between value-based and DT-based methods in dense reward studies. LaMo excels at learning in just a few trials, thanks to the inductive bias of previously trained LMs. While acknowledging some limitations, including the competitiveness of CQL and the loss of auxiliary language prediction, the study aims to inspire further exploration of larger LMs in offline RL.

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