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 2571–2580 of 15113 papers

TitleStatusHype
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning—0
Task-Agnostic Learning to Accomplish New Tasks—0
Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review—0
Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach—0
A Memory Efficient Deep Reinforcement Learning Approach For Snake Game Autonomous Agents—0
Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range—0
Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning—0
A Memory-Based Reinforcement Learning Approach to Integrated Sensing and Communication—0
A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling—0
Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents—0
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Benchmark Results

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