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 10201–10250 of 15113 papers

TitleStatusHype
Neural Math Word Problem Solver with Reinforcement Learning—0
Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning—0
Neural Network Approximation for Pessimistic Offline Reinforcement Learning—0
Neural Network Based Model Predictive Control for an Autonomous Vehicle—0
Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction—0
Neural Network Compatible Off-Policy Natural Actor-Critic Algorithm—0
Neural Network Compression for Reinforcement Learning Tasks—0
Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation—0
Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations—0
Neural Network Pruning Through Constrained Reinforcement Learning—0
Neural networks with motivation—0
Neural Network Verification in Control—0
Neural NID Rules—0
Neural Optimizer Search using Reinforcement Learning—0
Neural Ordinary Differential Equation Value Networks for Parametrized Action Spaces—0
Neural Packet Classification—0
Neural Packing: from Visual Sensing to Reinforcement Learning—0
Neural Program Planner for Structured Predictions—0
Neural Program Synthesis By Self-Learning—0
Neural-Progressive Hedging: Enforcing Constraints in Reinforcement Learning with Stochastic Programming—0
Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy—0
Neural Recursive Belief States in Multi-Agent Reinforcement Learning—0
Neural Task Graph Execution—0
Neural Temporal-Difference Learning Converges to Global Optima—0
Neural Text Generation: Past, Present and Beyond—0
Neural Topic Model with Reinforcement Learning—0
Neural-to-Tree Policy Distillation with Policy Improvement Criterion—0
Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy—0
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment—0
NeurIPS 2022 Competition: Driving SMARTS—0
NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding—0
Neuroevolution-Based Inverse Reinforcement Learning—0
Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling—0
Neuromuscular Reinforcement Learning to Actuate Human Limbs through FES—0
Neuron Activation Analysis for Multi-Joint Robot Reinforcement Learning—0
Neuron as an Agent—0
Neuroprospecting with DeepRL agents—0
Neuro-Symbolic Hierarchical Rule Induction—0
Neuro-symbolic Meta Reinforcement Learning for Trading—0
Neurosymbolic Reinforcement Learning and Planning: A Survey—0
Neuro-Symbolic Reinforcement Learning with First-Order Logic—0
Neuro-Symbolic World Models for Adapting to Open World Novelty—0
NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video Streaming—0
Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions—0
New Auction Algorithms for Path Planning, Network Transport, and Reinforcement Learning—0
New Challenges in Reinforcement Learning: A Survey of Security and Privacy—0
New Reinforcement Learning Using a Chaotic Neural Network for Emergence of "Thinking" - "Exploration" Grows into "Thinking" through Learning -—0
News-based trading strategies—0
Next-Future: Sample-Efficient Policy Learning for Robotic-Arm Tasks—0
N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs—0
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Benchmark Results

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