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 4081–4090 of 15113 papers

TitleStatusHype
Actions Speak What You Want: Provably Sample-Efficient Reinforcement Learning of the Quantal Stackelberg Equilibrium from Strategic Feedbacks—0
Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning—0
Active Alignments of Lens Systems with Reinforcement Learning—0
Active Classification of Moving Targets with Learned Control Policies—0
Active Coverage for PAC Reinforcement Learning—0
Active Deep Q-learning with Demonstration—0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
Active Hierarchical Imitation and Reinforcement Learning—0
Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learning—0
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Benchmark Results

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