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 2641–2650 of 15113 papers

TitleStatusHype
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
Actor-Critics Can Achieve Optimal Sample Efficiency—0
Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems—0
Automated Hybrid Reward Scheduling via Large Language Models for Robotic Skill Learning—0
Online Phase Estimation of Human Oscillatory Motions using Deep Learning—0
EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-TuningCode0
Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study—0
Prompt-responsive Object Retrieval with Memory-augmented Student-Teacher Learning—0
Analytic Energy-Guided Policy Optimization for Offline Reinforcement Learning—0
A Generalised and Adaptable Reinforcement Learning Stopping MethodCode0
Show:102550
← PrevPage 265 of 1512Next →

Benchmark Results

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