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 9076–9100 of 15113 papers

TitleStatusHype
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic—0
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States—0
R-LAtte: Attention Module for Visual Control via Reinforcement Learning—0
Sample efficient Quality Diversity for neural continuous control—0
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization—0
Offline Policy Optimization with Variance Regularization—0
Monte-Carlo Planning and Learning with Language Action Value Estimates—0
Reinforcement Learning with Bayesian Classifiers: Efficient Skill Learning from Outcome Examples—0
Optimistic Policy Optimization with General Function Approximations—0
Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates—0
PODS: Policy Optimization via Differentiable Simulation—0
What are the Statistical Limits of Batch RL with Linear Function Approximation?—0
Learning a Transferable Scheduling Policy for Various Vehicle Routing Problems based on Graph-centric Representation Learning—0
Attention-driven Robotic Manipulation—0
Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization—0
Learning Active Learning in the Batch-Mode Setup with Ensembles of Active Learning Agents—0
Coordinated Multi-Agent Exploration Using Shared Goals—0
Improving Learning to Branch via Reinforcement Learning—0
Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning—0
Communication in Multi-Agent Reinforcement Learning: Intention Sharing—0
Distributional Reinforcement Learning for Risk-Sensitive Policies—0
Aspect-based Sentiment Classification via Reinforcement Learning—0
Grounding Language to Entities for Generalization in Reinforcement Learning—0
Learning Latent Landmarks for Generalizable Planning—0
Adaptive Learning Rates for Multi-Agent Reinforcement Learning—0
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Benchmark Results

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