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 10476–10500 of 15113 papers

TitleStatusHype
Online Meta-learning by Parallel Algorithm Competition—0
Online Model-Free Reinforcement Learning for the Automatic Control of a Flexible Wing Aircraft—0
Online Model Selection for Reinforcement Learning with Function Approximation—0
Online Monotone Games—0
Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control—0
Online Multimodal Transportation Planning using Deep Reinforcement Learning—0
Online Observer-Based Inverse Reinforcement Learning—0
Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization—0
Online Phase Estimation of Human Oscillatory Motions using Deep Learning—0
Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams—0
Online Policies for Real-Time Control Using MRAC-RL—0
Online Policy Optimization for Robust MDP—0
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning—0
Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven—0
Online Reinforcement Learning for Periodic MDP—0
Online Reinforcement Learning for Real-Time Exploration in Continuous State and Action Markov Decision Processes—0
Online Reinforcement Learning in Periodic MDP—0
Online Reinforcement Learning in Stochastic Games—0
Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes—0
On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization—0
Online Reinforcement Learning with Uncertain Episode Lengths—0
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints—0
Online RL in Linearly q^π-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore—0
Online Robustness Training for Deep Reinforcement Learning—0
Online Robust Policy Learning in the Presence of Unknown Adversaries—0
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Benchmark Results

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