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

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Showing 10776–10800 of 15113 papers

TitleStatusHype
Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning—0
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation—0
Goal-conditioned Batch Reinforcement Learning for Rotation Invariant Locomotion—0
Show Us the Way: Learning to Manage Dialog from Demonstrations—0
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction LibraryCode1
Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions—0
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence GenerationCode0
Data-Driven Robust Control Using Reinforcement Learning—0
A Game Theoretic Framework for Model Based Reinforcement Learning—0
Fast Template Matching and Update for Video Object Tracking and SegmentationCode1
Analyzing Reinforcement Learning Benchmarks with Random Weight GuessingCode0
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks—0
Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making—0
D4RL: Datasets for Deep Data-Driven Reinforcement LearningCode2
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning—0
ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos—0
Zero-Shot Compositional Policy Learning via Language GroundingCode1
Prolog Technology Reinforcement Learning ProverCode1
Reinforcement Learning in a Physics-Inspired Semi-Markov EnvironmentCode0
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems—0
Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems—0
A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions—0
Extrapolation in Gridworld Markov-Decision Processes—0
A reinforcement learning application of guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapy—0
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Benchmark Results

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