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 1321–1330 of 15113 papers

TitleStatusHype
Approximate Equivariance in Reinforcement Learning—0
A Comparative Study of Deep Reinforcement Learning for Crop Production Management—0
Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems—0
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics DataCode0
An Open-source Sim2Real Approach for Sensor-independent Robot Navigation in a GridCode0
When to Localize? A Risk-Constrained Reinforcement Learning Approach—0
Embedding Safety into RL: A New Take on Trust Region Methods—0
Pre-trained Visual Dynamics Representations for Efficient Policy Learning—0
Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation—0
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Benchmark Results

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