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 2276–2300 of 15113 papers

TitleStatusHype
Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization—0
Heuristic Algorithm-based Action Masking Reinforcement Learning (HAAM-RL) with Ensemble Inference Method—0
Constrained Reinforcement Learning with Smoothed Log Barrier Function—0
Policy Mirror Descent with LookaheadCode0
Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning—0
Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network—0
Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study—0
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes—0
Policy Bifurcation in Safe Reinforcement LearningCode1
HYDRA: A Hyper Agent for Dynamic Compositional Visual ReasoningCode1
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path PlanningCode2
Fast Value Tracking for Deep Reinforcement Learning—0
Reinforcement Learning with Generalizable Gaussian Splatting—0
Efficient Transformer-based Hyper-parameter Optimization for Resource-constrained IoT EnvironmentsCode0
Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers—0
Decomposing Control Lyapunov Functions for Efficient Reinforcement LearningCode0
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight—0
EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents—0
Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data—0
Offline Multitask Representation Learning for Reinforcement Learning—0
State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards—0
The Value of Reward Lookahead in Reinforcement Learning—0
Reinforcement Learning with Token-level Feedback for Controllable Text GenerationCode1
Causality from Bottom to Top: A Survey—0
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation—0
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Benchmark Results

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