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 2721–2730 of 15113 papers

TitleStatusHype
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement LearningCode0
Unveiling the Implicit Toxicity in Large Language ModelsCode1
Safe Reinforcement Learning in a Simulated Robotic Arm—0
Two-step dynamic obstacle avoidanceCode0
An Investigation of Time Reversal Symmetry in Reinforcement LearningCode0
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy—0
Optimal Observer Design Using Reinforcement Learning and Quadratic Neural Networks—0
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning—0
A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning—0
Replay across Experiments: A Natural Extension of Off-Policy RL—0
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Benchmark Results

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