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 7676–7700 of 15113 papers

TitleStatusHype
Model-based Policy Search for Partially Measurable Systems—0
Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks—0
Model-based Reinforcement Learning and the Eluder Dimension—0
Model-based Reinforcement Learning: A Survey—0
Model-Based Reinforcement Learning Exploiting State-Action Equivalence—0
Model-based reinforcement learning for biological sequence design—0
Model-based Reinforcement Learning for Predictions and Control for Limit Order Books—0
Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements—0
Model-Based Reinforcement Learning for Approximate Optimal Control with Temporal Logic Specifications—0
Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level—0
Model-Based Reinforcement Learning via Stochastic Hybrid Models—0
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
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Benchmark Results

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