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 431–440 of 15113 papers

TitleStatusHype
Developing and Integrating Trust Modeling into Multi-Objective Reinforcement Learning for Intelligent Agricultural Management—0
Attention-Based Reward Shaping for Sparse and Delayed RewardsCode0
Beyond 'Aha!': Toward Systematic Meta-Abilities Alignment in Large Reasoning ModelsCode2
ImagineBench: Evaluating Reinforcement Learning with Large Language Model RolloutsCode1
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation—0
Reinforcing the Diffusion Chain of Lateral Thought with Diffusion Language Models—0
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change—0
Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps—0
Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systems—0
Show:102550
← PrevPage 44 of 1512Next →

Benchmark Results

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