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 3321–3330 of 15113 papers

TitleStatusHype
Heterogeneous Knowledge for Augmented Modular Reinforcement Learning—0
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control—0
Normalization Enhances Generalization in Visual Reinforcement LearningCode0
Identifiability and Generalizability in Constrained Inverse Reinforcement LearningCode0
Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning—0
Improving and Benchmarking Offline Reinforcement Learning AlgorithmsCode1
Non-stationary Reinforcement Learning under General Function Approximation—0
Safe Offline Reinforcement Learning with Real-Time Budget ConstraintsCode1
TorchRL: A data-driven decision-making library for PyTorchCode4
Efficient Diffusion Policies for Offline Reinforcement LearningCode1
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Benchmark Results

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