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 7301–7325 of 15113 papers

TitleStatusHype
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
FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control—0
Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning—0
Fast Value Tracking for Deep Reinforcement Learning—0
Fault-Aware Robust Control via Adversarial Reinforcement Learning—0
Fault-Tolerant Control of Degrading Systems with On-Policy Reinforcement Learning—0
FDPP: Fine-tune Diffusion Policy with Human Preference—0
Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning—0
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments—0
Feasible Policy Iteration for Safe Reinforcement Learning—0
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Benchmark Results

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