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 5101–5125 of 15113 papers

TitleStatusHype
A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes—0
Unified Automatic Control of Vehicular Systems with Reinforcement LearningCode1
Solving the vehicle routing problem with deep reinforcement learning—0
Reinforcement learning with experience replay and adaptation of action dispersion—0
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity AnalysisCode0
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions—0
Meta Reinforcement Learning with Successor Feature Based Context—0
Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content—0
Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain RandomizationCode0
Deep Reinforcement Learning for System-on-Chip: Myths and Realities—0
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Graph Inverse Reinforcement Learning from Diverse Videos—0
Latent Properties of Lifelong Learning Systems—0
RangL: A Reinforcement Learning Competition Platform—0
Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning—0
Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits—0
POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning—0
Multi-Objective Provisioning of Network Slices using Deep Reinforcement Learning—0
Structural Similarity for Improved Transfer in Reinforcement Learning—0
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control—0
Dynamic Shielding for Reinforcement Learning in Black-Box Environments—0
A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation—0
Safe and Robust Experience Sharing for Deterministic Policy Gradient AlgorithmsCode0
Unsupervised Training for Neural TSP Solver—0
Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning—0
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Benchmark Results

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