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 9151–9175 of 15113 papers

TitleStatusHype
Probing the Robustness of Trained Metrics for Conversational Dialogue Systems—0
Probing Transfer in Deep Reinforcement Learning without Task Engineering—0
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs—0
Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning—0
Processing Network Controls via Deep Reinforcement Learning—0
Process Supervision-Guided Policy Optimization for Code Generation—0
Production-based Cognitive Models as a Test Suite for Reinforcement Learning Algorithms—0
Product Title Refinement via Multi-Modal Generative Adversarial Learning—0
Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming—0
Profitable Strategy Design by Using Deep Reinforcement Learning for Trades on Cryptocurrency Markets—0
Programmable Control of Ultrasound Swarmbots through Reinforcement Learning—0
Programmatically Interpretable Reinforcement Learning—0
Programmatic Policy Extraction by Iterative Local Search—0
Programmatic Reinforcement Learning without Oracles—0
Programmatic Reward Design by Example—0
Program Synthesis Through Reinforcement Learning Guided Tree Search—0
Progress and summary of reinforcement learning on energy management of MPS-EV—0
Progressive extension of reinforcement learning action dimension for asymmetric assembly tasks—0
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control—0
Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning—0
PROGRESSOR: A Perceptually Guided Reward Estimator with Self-Supervised Online Refinement—0
Projected Natural Actor-Critic—0
Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning—0
Projected State-action Balancing Weights for Offline Reinforcement Learning—0
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data—0
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Benchmark Results

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