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 4126–4150 of 15113 papers

TitleStatusHype
Heuristic Algorithm-based Action Masking Reinforcement Learning (HAAM-RL) with Ensemble Inference Method—0
Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regression—0
Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning—0
Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study—0
Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network—0
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes—0
Fast Value Tracking for Deep Reinforcement Learning—0
Efficient Transformer-based Hyper-parameter Optimization for Resource-constrained IoT EnvironmentsCode0
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight—0
Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers—0
EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents—0
Decomposing Control Lyapunov Functions for Efficient Reinforcement LearningCode0
The Value of Reward Lookahead in Reinforcement Learning—0
State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards—0
Offline Multitask Representation Learning for Reinforcement Learning—0
Reinforcement Learning with Generalizable Gaussian Splatting—0
Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data—0
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation—0
Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective—0
Causality from Bottom to Top: A Survey—0
Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC—0
Neural-Kernel Conditional Mean Embeddings—0
The Fallacy of Minimizing Cumulative Regret in the Sequential Task Setting—0
ViSaRL: Visual Reinforcement Learning Guided by Human Saliency—0
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement LearningCode0
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Benchmark Results

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