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 6426–6450 of 15113 papers

TitleStatusHype
A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes—0
Deep Reinforcement Learning for System-on-Chip: Myths and Realities—0
Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain RandomizationCode0
Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content—0
Meta Reinforcement Learning with Successor Feature Based Context—0
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions—0
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity AnalysisCode0
Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits—0
Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning—0
RangL: A Reinforcement Learning Competition Platform—0
Latent Properties of Lifelong Learning Systems—0
Graph Inverse Reinforcement Learning from Diverse Videos—0
Dynamic Shielding for Reinforcement Learning in Black-Box Environments—0
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control—0
A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation—0
POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning—0
Structural Similarity for Improved Transfer in Reinforcement Learning—0
Multi-Objective Provisioning of Network Slices using Deep Reinforcement Learning—0
Safe and Robust Experience Sharing for Deterministic Policy Gradient AlgorithmsCode0
Unsupervised Training for Neural TSP Solver—0
Semi-analytical Industrial Cooling System Model for Reinforcement Learning—0
Offline Reinforcement Learning at Multiple Frequencies—0
Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature—0
Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning—0
Live in the Moment: Learning Dynamics Model Adapted to Evolving PolicyCode0
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Benchmark Results

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