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 3881–3890 of 15113 papers

TitleStatusHype
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading—0
NeoRL: Efficient Exploration for Nonepisodic RL—0
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond—0
Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation—0
Causal prompting model-based offline reinforcement learning—0
Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming—0
A Digital Twin Framework for Reinforcement Learning with Real-Time Self-Improvement via Human Assistive Teleoperation—0
Crafting a Pogo Stick in Minecraft with Heuristic Search (Extended Abstract)Code0
Bayesian Design Principles for Offline-to-Online Reinforcement LearningCode0
Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling—0
Show:102550
← PrevPage 389 of 1512Next →

Benchmark Results

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