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 7951–7975 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
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Benchmark Results

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