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 3481–3490 of 15113 papers

TitleStatusHype
Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market—0
Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task—0
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment—0
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—0
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
Deep Q-Network for AI Soccer—0
A Strong Baseline for Batch Imitation Learning—0
Deep Q-network using reservoir computing with multi-layered readout—0
Counterfactual Credit Assignment in Model-Free Reinforcement Learning—0
A physics-informed reinforcement learning approach for the interfacial area transport in two-phase flow—0
Show:102550
← PrevPage 349 of 1512Next →

Benchmark Results

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