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 10401–10425 of 15113 papers

TitleStatusHype
Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error—0
Off-Policy Risk-Sensitive Reinforcement Learning Based Constrained Robust Optimal Control—0
Off-Policy Selection for Initiating Human-Centric Experimental Design—0
Off-Policy Self-Critical Training for Transformer in Visual Paragraph Generation—0
Off-Policy Shaping Ensembles in Reinforcement Learning—0
OffRIPP: Offline RL-based Informative Path Planning—0
Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning—0
Offsetting Unequal Competition through RL-assisted Incentive Schemes—0
OffWorld Gym: open-access physical robotics environment for real-world reinforcement learning benchmark and research—0
Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents—0
OIL: Observational Imitation Learning—0
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions—0
O-MAPL: Offline Multi-agent Preference Learning—0
Omega-Regular Objectives in Model-Free Reinforcement Learning—0
Omega-Regular Reward Machines—0
OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment—0
OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras—0
OmniRL: In-Context Reinforcement Learning by Large-Scale Meta-Training in Randomized Worlds—0
On- and Off-Policy Monotonic Policy Improvement—0
On Applications of Bootstrap in Continuous Space Reinforcement Learning—0
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods—0
On Bellman equations for continuous-time policy evaluation I: discretization and approximation—0
On Bellman's principle of optimality and Reinforcement learning for safety-constrained Markov decision process—0
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection—0
On Computation and Generalization of Generative Adversarial Imitation Learning—0
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Benchmark Results

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