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 1451–1500 of 15113 papers

TitleStatusHype
Solving Continuous Control with Episodic MemoryCode1
Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image TranslationCode1
Randomized Exploration for Reinforcement Learning with General Value Function ApproximationCode1
Deep Reinforcement Learning for Conservation DecisionsCode1
rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot SoccerCode1
Learning Intrusion Prevention Policies through Optimal StoppingCode1
Efficient (Soft) Q-Learning for Text Generation with Limited Good DataCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Reinforcement Learning as One Big Sequence Modeling ProblemCode1
Deep Reinforcement Learning based Group Recommender SystemCode1
A Minimalist Approach to Offline Reinforcement LearningCode1
Recomposing the Reinforcement Learning Building Blocks with HypernetworksCode1
A Game-Theoretic Approach to Multi-Agent Trust Region OptimizationCode1
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor RepresentationCode1
WAX-ML: A Python library for machine learning and feedback loops on streaming dataCode1
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RLCode1
Pretrained Encoders are All You NeedCode1
Pretraining Representations for Data-Efficient Reinforcement LearningCode1
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement LearningCode1
Dynamic Sparse Training for Deep Reinforcement LearningCode1
Learning Markov State Abstractions for Deep Reinforcement LearningCode1
Causal Influence Detection for Improving Efficiency in Reinforcement LearningCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Task-driven Semantic Coding via Reinforcement LearningCode1
Control-Oriented Model-Based Reinforcement Learning with Implicit DifferentiationCode1
ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learningCode1
Efficient Continuous Control with Double Actors and Regularized CriticsCode1
Distributional Reinforcement Learning with Unconstrained Monotonic Neural NetworksCode1
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement LearningCode1
Same State, Different Task: Continual Reinforcement Learning without InterferenceCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Online reinforcement learning with sparse rewards through an active inference capsuleCode1
Differentiable Architecture Search for Reinforcement LearningCode1
Offline Reinforcement Learning as One Big Sequence Modeling ProblemCode1
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement LearningCode1
Decision Transformer: Reinforcement Learning via Sequence ModelingCode1
Q-attention: Enabling Efficient Learning for Vision-based Robotic ManipulationCode1
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics MixtureCode1
Towards mental time travel: a hierarchical memory for reinforcement learning agentsCode1
Goal Misgeneralization in Deep Reinforcement LearningCode1
Robust Value Iteration for Continuous Control TasksCode1
Continual World: A Robotic Benchmark For Continual Reinforcement LearningCode1
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise SafetyCode1
Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous DrivingCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Gym-μRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement LearningCode1
Offline Meta Reinforcement Learning -- Identifiability Challenges and Effective Data Collection StrategiesCode1
Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point DetectionCode1
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Benchmark Results

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