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Found 9 papers, 3 papers with code
Can Large Language Models Play Games? A Case Study of A Self-Play Approach
Large Language Models (LLMs) harness extensive data from the Internet, storing a broad spectrum of prior knowledge.
How Can LLM Guide RL? A Value-Based Approach
Specifically, we develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning, particularly when the difference between the ideal policy and the LLM-informed policy is small, which suggests that the initial policy is close to optimal, reducing the need for further exploration.
A Principled Framework for Knowledge-enhanced Large Language Model
Large Language Models (LLMs) are versatile, yet they often falter in tasks requiring deep and reliable reasoning due to issues like hallucinations, limiting their applicability in critical scenarios.
Sample-Efficient Multi-Agent RL: An Optimization Perspective
We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation.
Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency
Specifically, we design a prompt template for reasoning that learns from the memory buffer and plans a future trajectory over a long horizon ("reason for future").
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
To achieve this, existing sample-efficient online RL algorithms typically consist of three components: estimation, planning, and exploration.
Guarded Policy Optimization with Imperfect Online Demonstrations
Assuming optimal, the teacher policy has the perfect timing and capability to intervene in the learning process of the student agent, providing safety guarantee and exploration guidance.
SPLID: Self-Imitation Policy Learning through Iterative Distillation
To this end, we propose a novel meta-algorithm Self-Imitation Policy Learning through Iterative Distillation (SPLID) which relies on the concept of $\delta$-distilled policy to iteratively level up the quality of the target data and agent mimics from the relabeled target data.
Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation
In generative adversarial imitation learning (GAIL), the agent aims to learn a policy from an expert demonstration so that its performance cannot be discriminated from the expert policy on a certain predefined reward set.
