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

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Showing 2851–2860 of 15113 papers

TitleStatusHype
SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process—0
SySLLM: Generating Synthesized Policy Summaries for Reinforcement Learning Agents Using Large Language Models—0
Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Robotics—0
DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey—0
Representation-based Reward Modeling for Efficient Safety Alignment of Large Language Model—0
Scalable Evaluation of Online Facilitation Strategies via Synthetic Simulation of DiscussionsCode0
Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach—0
MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics—0
Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds—0
Evaluating Reinforcement Learning Safety and Trustworthiness in Cyber-Physical Systems—0
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Benchmark Results

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