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

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Showing 9726–9750 of 15113 papers

TitleStatusHype
Model-Based Imitation Learning Using Entropy Regularization of Model and Policy—0
Model-Based Inverse Reinforcement Learning from Visual Demonstrations—0
Model-based Lookahead Reinforcement Learning—0
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models—0
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping—0
Model based Multi-agent Reinforcement Learning with Tensor Decompositions—0
Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects—0
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity—0
Model-Based Offline Meta-Reinforcement Learning with Regularization—0
Model-Based Offline Planning—0
Model-based Offline Reinforcement Learning with Local Misspecification—0
Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation—0
Model Based Planning with Energy Based Models—0
Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation—0
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
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Benchmark Results

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