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 9751–9775 of 15113 papers

TitleStatusHype
Model-based reinforcement learning for protein backbone design—0
Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows—0
Model Based Reinforcement Learning for Atari—0
Model-Based Reinforcement Learning for Sepsis Treatment—0
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control—0
Whole-Chain Recommendations—0
Model-based Reinforcement Learning from Signal Temporal Logic Specifications—0
Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games—0
Model-Based Reinforcement Learning via Imagination with Derived Memory—0
Model-Based Reinforcement Learning via Meta-Policy Optimization—0
Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration—0
Model-based Reinforcement Learning with Ensembled Model-value Expansion—0
Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation—0
Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network—0
Model-Based Reinforcement Learning with SINDy—0
Model-Based Reinforcement Learning with Value-Targeted Regression—0
Model Based Residual Policy Learning with Applications to Antenna Control—0
Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds—0
Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehicles—0
Model-based Trajectory Stitching for Improved Offline Reinforcement Learning—0
Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning—0
Model Checking for Reinforcement Learning in Autonomous Driving: One Can Do More Than You Think!—0
Model Embedding Model-Based Reinforcement Learning—0
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation—0
Model Ensemble-Based Intrinsic Reward for Sparse Reward Reinforcement Learning—0
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Benchmark Results

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