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 4901–4950 of 15113 papers

TitleStatusHype
Reward Shaping via Diffusion Process in Reinforcement Learning—0
Adaptive Ordered Information Extraction with Deep Reinforcement LearningCode0
On the Model-Misspecification in Reinforcement Learning—0
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agentsCode0
Enhancing variational quantum state diagonalization using reinforcement learning techniquesCode0
Acceleration in Policy Optimization—0
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions—0
Genes in Intelligent AgentsCode0
Active Policy Improvement from Multiple Black-box OraclesCode0
Do as I can, not as I get—0
Bootstrapped Representations in Reinforcement Learning—0
Temporal Difference Learning with Experience Replay—0
Semi-Offline Reinforcement Learning for Optimized Text GenerationCode0
The False Dawn: Reevaluating Google's Reinforcement Learning for Chip Macro Placement—0
Real-Time Network-Level Traffic Signal Control: An Explicit Multiagent Coordination Method—0
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization—0
Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving—0
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Granger Causal Interaction Skill Chains—0
A reinforcement learning strategy for p-adaptation in high order solvers—0
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources—0
Off-policy Evaluation in Doubly Inhomogeneous EnvironmentsCode0
Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning—0
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning—0
Multi-market Energy Optimization with Renewables via Reinforcement Learning—0
Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care—0
Unified Off-Policy Learning to Rank: a Reinforcement Learning PerspectiveCode0
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning—0
DenseLight: Efficient Control for Large-scale Traffic Signals with Dense FeedbackCode0
Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement LearningCode0
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning—0
Kernelized Reinforcement Learning with Order Optimal Regret Bounds—0
Combining Reinforcement Learning and Barrier Functions for Adaptive Risk Management in Portfolio Optimization—0
ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles—0
Diverse Projection Ensembles for Distributional Reinforcement Learning—0
Robust Reinforcement Learning through Efficient Adversarial Herding—0
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds—0
Online Prototype Alignment for Few-shot Policy TransferCode0
Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning—0
PEAR: Primitive enabled Adaptive Relabeling for boosting Hierarchical Reinforcement Learning—0
Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel—0
The Role of Diverse Replay for Generalisation in Reinforcement Learning—0
Learning Not to Spoof—0
Approximate information state based convergence analysis of recurrent Q-learning—0
Iteratively Refined Behavior Regularization for Offline Reinforcement Learning—0
Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation—0
Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning—0
Timing Process Interventions with Causal Inference and Reinforcement Learning—0
State Regularized Policy Optimization on Data with Dynamics Shift—0
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Benchmark Results

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