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 12051–12100 of 15113 papers

TitleStatusHype
Multiagent Reinforcement Learning in Games with an Iterated Dominance Solution—0
QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error—0
Policy Optimization by Local Improvement through Search—0
Reinforcement learning for suppression of collective activity in oscillatory ensembles—0
Training a Constrained Natural Media Painting Agent using Reinforcement Learning—0
Temporal Difference Weighted Ensemble For Reinforcement Learning—0
Model Ensemble-Based Intrinsic Reward for Sparse Reward Reinforcement Learning—0
Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning—0
Model-free Learning Control of Nonlinear Stochastic Systems with Stability Guarantee—0
Self-Supervised State-Control through Intrinsic Mutual Information RewardsCode0
Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach—0
Meta Learning via Learned Loss—0
Sequence-level Intrinsic Exploration Model for Partially Observable Domains—0
Model Imitation for Model-Based Reinforcement Learning—0
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning—0
Zero-Shot Policy Transfer with Disentangled Attention—0
REFINING MONTE CARLO TREE SEARCH AGENTS BY MONTE CARLO TREE SEARCH—0
Subjective Reinforcement Learning for Open Complex Environments—0
ROBEL: Robotics Benchmarks for Learning with Low-Cost RobotsCode0
Variational Constrained Reinforcement Learning with Application to Planning at Roundabout—0
S2VG: Soft Stochastic Value Gradient method—0
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees—0
Policy Tree Network—0
Solving single-objective tasks by preference multi-objective reinforcement learning—0
Partial Simulation for Imitation Learning—0
Multi-Agent Hierarchical Reinforcement Learning for Humanoid Navigation—0
Striving for Simplicity in Off-Policy Deep Reinforcement Learning—0
Reinforcement Learning with Chromatic Networks—0
Sparse Skill Coding: Learning Behavioral Hierarchies with Sparse Codes—0
Mint: Matrix-Interleaving for Multi-Task Learning—0
Stabilizing Off-Policy Reinforcement Learning with Conservative Policy Gradients—0
Robust Domain Randomization for Reinforcement Learning—0
Pre-training as Batch Meta Reinforcement Learning with tiMe—0
Paying Attention to Function WordsCode0
Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches—0
Controlling an Autonomous Vehicle with Deep Reinforcement Learning—0
Avoidance Learning Using Observational Reinforcement Learning—0
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in ClutterCode0
Efficient Inference and Exploration for Reinforcement Learning—0
Active inference: demystified and comparedCode0
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement LearningCode0
Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network—0
PAC Reinforcement Learning without Real-World Feedback—0
Constrained Attractor Selection Using Deep Reinforcement Learning—0
Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization—0
Where to Look Next: Unsupervised Active Visual Exploration on 360° Input—0
Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze—0
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement LearningCode0
Modular Deep Reinforcement Learning with Temporal Logic SpecificationsCode0
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?—0
Show:102550
← PrevPage 242 of 303Next →

Benchmark Results

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