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 5576–5600 of 15113 papers

TitleStatusHype
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics—0
Inverse Reinforcement Learning: A Control Lyapunov Approach—0
Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning—0
Inverse reinforcement learning conditioned on brain scan—0
Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning—0
Inverse Reinforcement Learning for Marketing—0
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems—0
Inverse Reinforcement Learning for Strategy Identification—0
Inverse Reinforcement Learning for Text Summarization—0
Inverse Reinforcement Learning from a Gradient-based Learner—0
Graph Inverse Reinforcement Learning from Diverse Videos—0
Inverse Reinforcement Learning from Summary Data—0
Inverse Reinforcement Learning in Large State Spaces via Function Approximation—0
Inverse Reinforcement Learning in Swarm Systems—0
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees—0
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities—0
WFA-IRL: Inverse Reinforcement Learning of Autonomous Behaviors Encoded as Weighted Finite Automata—0
Inverse Reinforcement Learning through Structured Classification—0
Inverse Reinforcement Learning Under Noisy Observations—0
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling—0
Inverse Reinforcement Learning via Deep Gaussian Process—0
Inverse Reinforcement Learning via Matching of Optimality Profiles—0
Inverse Reinforcement Learning with Conditional Choice Probabilities—0
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics—0
Inverse Reinforcement Learning with Explicit Policy Estimates—0
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Benchmark Results

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