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 4551–4575 of 15113 papers

TitleStatusHype
Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents—0
Closed Drafting as a Case Study for First-Principle Interpretability, Memory, and Generalizability in Deep Reinforcement Learning—0
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving—0
A Tractable Inference Perspective of Offline RL—0
Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement—0
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics—0
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation—0
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation—0
Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households—0
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning—0
Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game—0
Behavior Alignment via Reward Function Optimization—0
Benchmark Generation Framework with Customizable Distortions for Image Classifier RobustnessCode0
Unsupervised Behavior Extraction via Random Intent Priors—0
Robust Offline Reinforcement learning with Heavy-Tailed RewardsCode0
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strike—0
Bridging Distributionally Robust Learning and Offline RL: An Approach to Mitigate Distribution Shift and Partial Data CoverageCode0
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion—0
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy EvaluationCode0
Demonstration-Regularized RL—0
CQM: Curriculum Reinforcement Learning with a Quantized World Model—0
Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning UpdatesCode0
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Benchmark Results

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