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 4611–4620 of 15113 papers

TitleStatusHype
Exploration with Principles for Diverse AI Supervision—0
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments—0
A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real BuildingsCode0
Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy ApproachCode0
Discerning Temporal Difference Learning—0
Novelty Detection in Reinforcement Learning with World Models—0
Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learningCode0
Virtual Augmented Reality for Atari Reinforcement LearningCode0
Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking—0
Off-Policy Evaluation for Human Feedback—0
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Benchmark Results

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