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 771–780 of 15113 papers

TitleStatusHype
SySLLM: Generating Synthesized Policy Summaries for Reinforcement Learning Agents Using Large Language Models—0
H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic—0
Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Robotics—0
SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process—0
NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models—0
DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey—0
Solving Bayesian inverse problems with diffusion priors and off-policy RL—0
Edge AI-Powered Real-Time Decision-Making for Autonomous Vehicles in Adverse Weather Conditions—0
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement LearningCode7
Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach—0
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Benchmark Results

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