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 541–550 of 15113 papers

TitleStatusHype
Monte Carlo Planning with Large Language Model for Text-Based Game Agents—0
Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator—0
Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows—0
Natural Policy Gradient for Average Reward Non-Stationary RL—0
Hybrid Reinforcement Learning and Model Predictive Control for Adaptive Control of Hydrogen-Diesel Dual-Fuel Combustion—0
Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints—0
TTRL: Test-Time Reinforcement LearningCode7
Tina: Tiny Reasoning Models via LoRACode3
SLiM-Gym: Reinforcement Learning for Population Genetics—0
Policy-Based Radiative Transfer: Solving the 2-Level Atom Non-LTE Problem using Soft Actor-Critic Reinforcement Learning—0
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Benchmark Results

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