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Found 9 papers, 2 papers with code
ImagineBench: Evaluating Reinforcement Learning with Large Language Model Rollouts
To bridge this gap, we introduce ImagineBench, the first comprehensive benchmark for evaluating offline RL algorithms that leverage both real rollouts and LLM-imaginary rollouts.
Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning
In this paper, we introduce the problem of RL from Composite Delayed Reward (RLCoDe), which generalizes traditional RL from delayed rewards by eliminating the strong assumption.
Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts
Furthermore, KALM effectively enables the LLM to comprehend environmental dynamics, resulting in the generation of meaningful imaginary rollouts that reflect novel skills and demonstrate the seamless integration of large language models and reinforcement learning.
Empowering Language Models with Active Inquiry for Deeper Understanding
The rise of large language models (LLMs) has revolutionized the way that we interact with artificial intelligence systems through natural language.
Language Model Self-improvement by Reinforcement Learning Contemplation
We demonstrate that SIRLC can be applied to various NLP tasks, such as reasoning problems, text generation, and machine translation.
Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation
To ease the learning burden of the policy, we investigate an inside-out scheme for natural language-conditioned RL by developing a task language (TL) that is task-related and unique.
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization
To tackle the issue of limited action execution in RL, this paper first formalizes the problem as a Sparse Action Markov Decision Process (SA-MDP), in which specific actions in the action space can only be executed for a limited time.
Regret Minimization Experience Replay in Off-Policy Reinforcement Learning
In reinforcement learning, experience replay stores past samples for further reuse.
Improving Fictitious Play Reinforcement Learning with Expanding Models
However, learning to beat a pool in stochastic games, i. e., a wide distribution over policy models, is either sample-consuming or insufficient to exploit all models with limited amount of samples.
