This AI Paper Has Moves: How Language Models Groove into Offline Reinforcement Learning with ‘LaMo’ Dance Steps and Few-Shot Learning

Researchers introduce Language Models for Motion Control (LaMo), a framework utilizing Large Language Models (LLMs) for offline reinforcement studying. It leverages pre-trained LLMs to reinforce RL coverage studying, using Decision Transformers (DT) initialized with LLMs and LoRA fine-tuning. LaMo outperforms present strategies in sparse-reward duties and narrows the hole between value-based offline RL and resolution transformers in dense-reward duties, notably excelling in eventualities with restricted knowledge samples.

Current analysis explores the synergy between transformers, notably DT, and LLMs for decision-making in RL duties. LLMs have beforehand proven promise in high-level job decomposition and coverage era. LaMo is a novel framework leveraging pre-trained LLMs for movement management duties, surpassing present strategies in sparse-reward eventualities and narrowing the hole between value-based offline RL and resolution transformers in dense-reward duties. It builds upon prior work like Wiki-RL, aiming to raised harness pre-trained LMs for offline RL.

The strategy reframes RL as a conditional sequence modelling downside. LaMo outperforms present strategies by combining LLMs with DT and introduces improvements like LoRA fine-tuning, non-linear MLP projections, and auxiliary language loss. It excels in sparse-reward duties and narrows the efficiency hole between value-based and DT-based strategies in dense-reward eventualities.

The LaMo framework for offline Reinforcement Learning incorporates pre-trained LMs and DTs. It enhances illustration studying with Multi-Layer Perceptrons and employs LoRA fine-tuning with an auxiliary language prediction loss to mix LMs’ information successfully. Extensive experiments throughout numerous duties and environments assess efficiency below various knowledge ratios, evaluating it with robust RL baselines like CQL, IQL, TD3BC, BC, DT, and Wiki-RL.

The LaMo framework excels in sparse and dense-reward duties, surpassing Decision Transformer and Wiki-RL. It outperforms a number of robust RL baselines, together with CQL, IQL, TD3BC, BC, and DT, whereas avoiding overfitting—LaMo’s sturdy studying means, particularly with restricted knowledge, advantages from pre-trained LMs’ inductive bias. Evaluation of the D4RL benchmark and thorough ablation research verify the effectiveness of every part inside the framework.

The research wants an in-depth exploration of higher-level illustration studying strategies to reinforce full fine-tuning’s generalizability. Computational constraints restrict the examination of other approaches like joint coaching. The affect of various pre-training qualities of LMs past evaluating GPT-2, early-stopped pre-trained, and randomly shuffled pre-trained fashions nonetheless must be addressed. Specific numerical outcomes and efficiency metrics are required to substantiate claims of state-of-the-art efficiency and baseline superiority.

In conclusion, the LaMo framework makes use of pre-trained LMs for movement management in offline RL, attaining superior efficiency in sparse-reward duties in comparison with CQL, IQL, TD3BC, and DT. It narrows the efficiency hole between value-based and DT-based strategies in dense-reward research. LaMo excels in few-shot studying, due to the inductive bias from pre-trained LMs. While it acknowledges some limitations, together with CQL’s competitiveness and the auxiliary language prediction loss, the research goals to encourage additional exploration of bigger LMs in offline RL.

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