SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5751–5775 of 15113 papers

TitleStatusHype
Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games—0
Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints—0
Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization—0
Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game—0
Language-based General Action Template for Reinforcement Learning Agents—0
Language-Driven Temporal Activity Localization: A Semantic Matching Reinforcement Learning Model—0
Language Expansion In Text-Based Games—0
Language Guided Exploration for RL Agents in Text Environments—0
Language Inference with Multi-head Automata through Reinforcement Learning—0
LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning—0
LARES: Latent Reasoning for Sequential Recommendation—0
Large Language Model driven Policy Exploration for Recommender Systems—0
Large Language Model-Enhanced Reinforcement Learning for Generic Bus Holding Control Strategies—0
Large Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement Learning—0
Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings—0
Large Language Models Prompting With Episodic Memory—0
Large scale continuous-time mean-variance portfolio allocation via reinforcement learning—0
Large-scale Interactive Recommendation with Tree-structured Policy Gradient—0
Large-scale Regional Traffic Signal Control Based on Single-Agent Reinforcement Learning—0
Large-scale Reinforcement Learning for Diffusion Models—0
Large-Scale Retrieval for Reinforcement Learning—0
Large-Scale Traffic Signal Control by a Nash Deep Q-network Approach—0
Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified