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 11651–11700 of 15113 papers

TitleStatusHype
Explicit Planning for Efficient Exploration in Reinforcement Learning—0
A Family of Robust Stochastic Operators for Reinforcement Learning—0
Identifying Cognitive Radars -- Inverse Reinforcement Learning using Revealed Preferences—0
Propagating Uncertainty in Reinforcement Learning via Wasserstein BarycentersCode0
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement LearningCode0
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents—0
Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning—0
No-Press Diplomacy: Modeling Multi-Agent Gameplay—0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Privacy-Preserving Q-Learning with Functional Noise in Continuous SpacesCode0
Park: An Open Platform for Learning-Augmented Computer SystemsCode0
Neural Temporal-Difference Learning Converges to Global Optima—0
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex ObjectivesCode0
Regret Bounds for Learning State Representations in Reinforcement Learning—0
Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy—0
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional PoliciesCode0
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes—0
Mix and Match: Markov Chains & Mixing Times for Matching in Rideshare—0
IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks—0
Distributed Soft Actor-Critic with Multivariate Reward Representation and Knowledge DistillationCode0
Induction of Subgoal Automata for Reinforcement Learning—0
Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles—0
Simulation-based reinforcement learning for real-world autonomous drivingCode0
Playing Games in the Dark: An approach for cross-modality transfer in reinforcement learningCode0
Stigmergic Independent Reinforcement Learning for Multi-Agent Collaboration—0
Multi-Agent Deep Reinforcement Learning with Adaptive Policies—0
Improving Neural Relation Extraction with Positive and Unlabeled Learning—0
Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy OptimizationCode0
Augmented Random Search for Quadcopter Control: An alternative to Reinforcement Learning—0
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction—0
Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense—0
Improving Fictitious Play Reinforcement Learning with Expanding Models—0
GRIm-RePR: Prioritising Generating Important Features for Pseudo-Rehearsal—0
Towards Similarity Graphs Constructed by Deep Reinforcement LearningCode0
Restoring Chaos Using Deep Reinforcement Learning—0
Natural Language Generation Using Reinforcement Learning with External RewardsCode0
Multi-Vehicle Mixed-Reality Reinforcement Learning for Autonomous Multi-Lane Driving—0
Join Query Optimization with Deep Reinforcement Learning AlgorithmsCode0
Behavior Regularized Offline Reinforcement Learning—0
Control-Tutored Reinforcement Learning: an application to the Herding Problem—0
A General Framework on Enhancing Portfolio Management with Reinforcement Learning—0
A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control—0
End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit AffordancesCode0
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem—0
Biologically inspired architectures for sample-efficient deep reinforcement learning—0
Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks—0
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial ProblemsCode0
Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning—0
Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning—0
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms—0
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Benchmark Results

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