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 2681–2690 of 15113 papers

TitleStatusHype
Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator—0
Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows—0
Monte Carlo Planning with Large Language Model for Text-Based Game Agents—0
Hybrid Reinforcement Learning and Model Predictive Control for Adaptive Control of Hydrogen-Diesel Dual-Fuel Combustion—0
Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model—0
Insights from Verification: Training a Verilog Generation LLM with Reinforcement Learning with Testbench Feedback—0
StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation—0
SARI: Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning—0
Policy-Based Radiative Transfer: Solving the 2-Level Atom Non-LTE Problem using Soft Actor-Critic Reinforcement Learning—0
SLiM-Gym: Reinforcement Learning for Population Genetics—0
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Benchmark Results

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