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 7151–7175 of 15113 papers

TitleStatusHype
Variational Inference for Policy Gradient—0
Variational Inference MPC for Bayesian Model-based Reinforcement Learning—0
Variational Intrinsic Control Revisited—0
Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition—0
Variational Meta Reinforcement Learning for Social Robotics—0
Variational Model-based Policy Optimization—0
Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems—0
Variational oracle guiding for reinforcement learning—0
Variational Policy Gradient Method for Reinforcement Learning with General Utilities—0
Variational quantum compiling with double Q-learning—0
Parametrized quantum policies for reinforcement learning—0
Policy Gradients using Variational Quantum Circuits—0
Variational Quantum Reinforcement Learning via Evolutionary Optimization—0
Variational Quantum Soft Actor-Critic for Robotic Arm Control—0
Variational Regret Bounds for Reinforcement Learning—0
Variational Reward Estimator Bottleneck: Learning Robust Reward Estimator for Multi-Domain Task-Oriented Dialog—0
VAR-MATH: Probing True Mathematical Reasoning in Large Language Models via Symbolic Multi-Instance Benchmarks—0
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning—0
Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning—0
Vehicle Type Specific Waypoint Generation—0
Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning—0
Verifiable Reinforcement Learning Systems via Compositionality—0
Verification of Dissipativity and Evaluation of Storage Function in Economic Nonlinear MPC using Q-Learning—0
VerifierQ: Enhancing LLM Test Time Compute with Q-Learning-based Verifiers—0
VerifyBench: Benchmarking Reference-based Reward Systems for Large Language Models—0
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Benchmark Results

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