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 7951–8000 of 15113 papers

TitleStatusHype
Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings—0
Context Meta-Reinforcement Learning via NeuromodulationCode0
Adjacency constraint for efficient hierarchical reinforcement learning—0
Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner—0
A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles—0
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning—0
Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System—0
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL—0
Adaptive Discretization in Online Reinforcement Learning—0
Reinforced Workload Distribution Fairness—0
Mixed Cooperative-Competitive Communication Using Multi-Agent Reinforcement Learning—0
Open Problem: Tight Online Confidence Intervals for RKHS Elements—0
Efficient Meta Subspace OptimizationCode0
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision ProcessesCode0
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives—0
An Adaptable Approach to Learn Realistic Legged Locomotion without Examples—0
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning—0
Extracting Expert's Goals by What-if Interpretable Modeling—0
Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning—0
Data Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control—0
D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning—0
Comparing Heuristics, Constraint Optimization, and Reinforcement Learning for an Industrial 2D Packing Problem—0
Enhancing Reinforcement Learning with discrete interfaces to learn the Dyck Language—0
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning—0
Finite Horizon Q-learning: Stability, Convergence, Simulations and an application on Smart Grids—0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
A Subgame Perfect Equilibrium Reinforcement Learning Approach to Time-inconsistent Problems—0
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning—0
Stabilising viscous extensional flows using Reinforcement LearningCode0
APPTeK: Agent-Based Predicate Prediction in Temporal Knowledge Graphs—0
Model based Multi-agent Reinforcement Learning with Tensor Decompositions—0
Transfer learning with causal counterfactual reasoning in Decision Transformers—0
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection—0
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions—0
Towards Hyperparameter-free Policy Selection for Offline Reinforcement LearningCode0
The Difficulty of Passive Learning in Deep Reinforcement Learning—0
Multi-Agent Advisor Q-LearningCode0
Fragment-based Sequential Translation for Molecular Optimization—0
Average-Reward Learning and Planning with Options—0
Distributional Reinforcement Learning for Multi-Dimensional Reward FunctionsCode0
Accelerating Distributed Deep Reinforcement Learning by In-Network Experience Sampling—0
EnTRPO: Trust Region Policy Optimization Method with Entropy Regularization—0
Neural PPO-Clip Attains Global Optimality: A Hinge Loss Perspective—0
Automating Control of Overestimation Bias for Reinforcement Learning—0
Learning Robust Controllers Via Probabilistic Model-Based Policy Search—0
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey—0
Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Control—0
Learning What to Memorize: Using Intrinsic Motivation to Form Useful Memory in Partially Observable Reinforcement Learning—0
Can Q-Learning be Improved with Advice?—0
Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning—0
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Benchmark Results

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