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 3001–3025 of 15113 papers

TitleStatusHype
Identifiability and generalizability from multiple experts in Inverse Reinforcement LearningCode0
Hyperparameters in Contextual RL are Highly SituationalCode0
Hyperparameter Auto-tuning in Self-Supervised Robotic LearningCode0
Large Language Models are Biased Reinforcement LearnersCode0
Assessing Generalization in Deep Reinforcement LearningCode0
Contextual Imagined Goals for Self-Supervised Robotic LearningCode0
Hyp-RL : Hyperparameter Optimization by Reinforcement LearningCode0
Identifiability and Generalizability in Constrained Inverse Reinforcement LearningCode0
Hype or Heuristic? Quantum Reinforcement Learning for Join Order OptimisationCode0
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgentCode0
Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics DataCode0
Learning Actionable Representations with Goal-Conditioned PoliciesCode0
Hyperbolic Discounting and Learning over Multiple HorizonsCode0
Hybrid Latent Reasoning via Reinforcement LearningCode0
Hybrid Reinforcement Learning with Expert State SequencesCode0
Context Meta-Reinforcement Learning via NeuromodulationCode0
Hybridising Reinforcement Learning and Heuristics for Hierarchical Directed Arc Routing ProblemsCode0
Learning-based Model Predictive Control for Safe Exploration and Reinforcement LearningCode0
Hybrid Reward Architecture for Reinforcement LearningCode0
Weak Human Preference Supervision For Deep Reinforcement LearningCode0
Context-Aware Visual Policy Network for Sequence-Level Image CaptioningCode0
Hybrid Actor-Critic Reinforcement Learning in Parameterized Action SpaceCode0
Human-Level Control without Server-Grade HardwareCode0
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learningCode0
Human-Inspired Framework to Accelerate Reinforcement LearningCode0
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Benchmark Results

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