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 10126–10150 of 15113 papers

TitleStatusHype
Navigation with QPHIL: Quantizing Planner for Hierarchical Implicit Q-Learning—0
PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning—0
NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images—0
Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs—0
NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control—0
Nearly Horizon-Free Offline Reinforcement Learning—0
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game—0
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes—0
Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation—0
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes—0
Nearly Minimax Optimal Reward-free Reinforcement Learning—0
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee—0
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs—0
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model—0
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR—0
Near-Optimal Adversarial Reinforcement Learning with Switching Costs—0
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation—0
Near-Optimal Differentially Private Reinforcement Learning—0
Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments—0
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction—0
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism—0
Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities—0
Near-optimal Policy Identification in Active Reinforcement Learning—0
Near Optimal Policy Optimization via REPS—0
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning—0
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Benchmark Results

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