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 3671–3680 of 15113 papers

TitleStatusHype
Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning—0
Preference Transformer: Modeling Human Preferences using Transformers for RLCode1
Compensating for Sensing Failures via Delegation in Human-AI Hybrid Systems—0
Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications—0
Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity—0
A Deep Reinforcement Learning Trader without Offline Training—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement LearningCode1
Human-Inspired Framework to Accelerate Reinforcement LearningCode0
Parameter Optimization of LLC-Converter with multiple operation points using Reinforcement Learning—0
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Benchmark Results

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