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 7651–7675 of 15113 papers

TitleStatusHype
Model-Agnostic Learning to Meta-Learn—0
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks—0
Model-Based Actor-Critic with Chance Constraint for Stochastic System—0
Model-based adaptation for sample efficient transfer in reinforcement learning control of parameter-varying systems—0
Model-based Bayesian Reinforcement Learning for Dialogue Management—0
Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian—0
Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization—0
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey—0
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning—0
Model-Based Episodic Memory Induces Dynamic Hybrid Controls—0
Model-based imitation learning from state trajectories—0
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
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Benchmark Results

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