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 4481–4490 of 15113 papers

TitleStatusHype
Predictable Reinforcement Learning Dynamics through Entropy Rate MinimizationCode0
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control—0
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement LearningCode0
Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning—0
Two-Step Reinforcement Learning for Multistage Strategy Card Game—0
Q-learning Based Optimal False Data Injection Attack on Probabilistic Boolean Control Networks—0
Safe Reinforcement Learning in a Simulated Robotic Arm—0
Two-step dynamic obstacle avoidanceCode0
An Investigation of Time Reversal Symmetry in Reinforcement LearningCode0
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning—0
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Benchmark Results

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