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 7201–7250 of 15113 papers

TitleStatusHype
Stability Constrained Reinforcement Learning for Real-Time Voltage Control—0
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance—0
Reinforcement Learning with Information-Theoretic Actuation—0
Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement LearningCode1
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for AtariCode0
Scalable Online Planning via Reinforcement Learning Fine-TuningCode1
Solving the Real Robot Challenge using Deep Reinforcement LearningCode0
Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators—0
Surveillance Evasion Through Bayesian Reinforcement LearningCode0
A Privacy-preserving Distributed Training Framework for Cooperative Multi-agent Deep Reinforcement Learning—0
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning—0
Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
Generalized Maximum Entropy Reinforcement Learning via Reward Shaping—0
CubeTR: Learning to Solve the Rubik's Cube using Transformers—0
Policy improvement by planning with GumbelCode2
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning—0
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Variational oracle guiding for reinforcement learning—0
Plan Your Target and Learn Your Skills: State-Only Imitation Learning via Decoupled Policy Optimization—0
WaveCorr: Deep Reinforcement Learning with Permutation Invariant Policy Networks for Portfolio Management—0
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach—0
Particle Based Stochastic Policy Optimization—0
Value Refinement Network (VRN)—0
P4O: Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization—0
Understanding the Generalization Gap in Visual Reinforcement Learning—0
Uncertainty Regularized Policy Learning for Offline Reinforcement Learning—0
Triangular Dropout: Variable Network Width without Retraining—0
Meta Attention For Off-Policy Actor-Critic—0
Metrics Matter: A Closer Look on Self-Paced Reinforcement Learning—0
On Reward Maximization and Distribution Matching for Fine-Tuning Language Models—0
Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning—0
That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities—0
Model-based Reinforcement Learning with Ensembled Model-value Expansion—0
Stability and Generalisation in Batch Reinforcement Learning—0
Sequential Communication in Multi-Agent Reinforcement Learning—0
Self-Supervised Structured Representations for Deep Reinforcement Learning—0
Nested Policy Reinforcement Learning for Clinical Decision Support—0
MURO: Deployment Constrained Reinforcement Learning with Model-based Uncertainty Regularized Batch Optimization—0
Safe Exploration in Linear Equality Constraint—0
Can Reinforcement Learning Efficiently Find Stackelberg-Nash Equilibria in General-Sum Markov Games?—0
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis—0
An Attempt to Model Human Trust with Reinforcement Learning—0
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning—0
Boosted Curriculum Reinforcement Learning—0
Detecting Worst-case Corruptions via Loss Landscape Curvature in Deep Reinforcement Learning—0
Graph-Enhanced Exploration for Goal-oriented Reinforcement Learning—0
Learning to Solve Combinatorial Problems via Efficient Exploration—0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
Deep Learning of Intrinsically Motivated Options in the Arcade Learning Environment—0
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Benchmark Results

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