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 3201–3225 of 15113 papers

TitleStatusHype
Hindsight Credit AssignmentCode0
Hindsight Foresight Relabeling for Meta-Reinforcement LearningCode0
DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic RewardCode0
Active Policy Improvement from Multiple Black-box OraclesCode0
Confidence Aware Inverse Constrained Reinforcement LearningCode0
High-Throughput Distributed Reinforcement Learning via Adaptive Policy SynchronizationCode0
Highway Graph to Accelerate Reinforcement LearningCode0
Hindsight Learning for MDPs with Exogenous InputsCode0
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary RewardsCode0
Hierarchical Reinforcement Learning with the MAXQ Value Function DecompositionCode0
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement LearningCode0
Meta-Inverse Reinforcement Learning with Probabilistic Context VariablesCode0
Adversarial Skill Networks: Unsupervised Robot Skill Learning from VideoCode0
Data driven approach towards more efficient Newton-Raphson power flow calculation for distribution gridsCode0
Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement LearningCode0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
Meta-Reinforcement Learning in Broad and Non-Parametric EnvironmentsCode0
Hierarchical Reinforcement Learning via Advantage-Weighted Information MaximizationCode0
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage DatasetCode0
Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation ApproachCode0
Conditional Computation in Neural Networks for faster modelsCode0
Hierarchical Reinforcement Learning with Optimal Level Synchronization based on a Deep Generative ModelCode0
Hierarchical Text Generation and Planning for Strategic DialogueCode0
Hindsight policy gradientsCode0
How to Make Deep RL Work in PracticeCode0
Show:102550
← PrevPage 129 of 605Next →

Benchmark Results

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