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 10776–10800 of 15113 papers

TitleStatusHype
Fast Algorithms for L_-constrained S-rectangular Robust MDPs—0
Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation—0
Fast Approximate Solutions using Reinforcement Learning for Dynamic Capacitated Vehicle Routing with Time Windows—0
Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Exploration—0
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES—0
Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning—0
Faster Deep Q-learning using Neural Episodic Control—0
Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction—0
Faster Reinforcement Learning with Expert State Sequences—0
Faster Reinforcement Learning with Value Target Lower Bounding—0
Fastest Convergence for Q-learning—0
Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning—0
Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization—0
Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations—0
Fast Policy Learning through Imitation and Reinforcement—0
Fast Rates for the Regret of Offline Reinforcement Learning—0
Fast Reinforcement Learning for Anti-jamming Communications—0
Fast Reinforcement Learning for Energy-Efficient Wireless Communications—0
Fast reinforcement learning with generalized policy updates—0
Fast Reinforcement Learning with Incremental Gaussian Mixture Models—0
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning—0
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing—0
Fast Sequence Generation with Multi-Agent Reinforcement Learning—0
Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement Learning—0
Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning—0
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Benchmark Results

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