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

Showing 81768200 of 15113 papers

TitleStatusHype
Room Clearance with Feudal Hierarchical Reinforcement Learning0
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence0
An Efficient Application of Neuroevolution for Competitive Multiagent LearningCode0
Continual World: A Robotic Benchmark For Continual Reinforcement LearningCode1
Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication0
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise SafetyCode1
Stochastic Approximation of Gaussian Free Energy for Risk-Sensitive Reinforcement Learning0
Offline Meta Reinforcement Learning -- Identifiability Challenges and Effective Data Collection StrategiesCode1
Meta Reinforcement Learning for Fast Adaptation of Hierarchical Policies0
Reinforcement learning for instance segmentation with high-level priors0
Reinforcement Learning based Disease Progression Model for Alzheimer’s Disease0
Revisiting Design Choices in Offline Model Based Reinforcement Learning0
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap0
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning0
Gym-μRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement LearningCode1
Certification of Iterative Predictions in Bayesian Neural NetworksCode0
De-Biased Modelling of Search Click Behavior with Reinforcement Learning0
Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous DrivingCode1
Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games0
Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using Reinforcement Learning0
Rule Augmented Unsupervised Constituency ParsingCode0
RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search0
On the use of feature-maps and parameter control for improved quality-diversity meta-evolution0
On Instrumental Variable Regression for Deep Offline Policy EvaluationCode0
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Benchmark Results

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