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 4591–4600 of 15113 papers

TitleStatusHype
An Algorithmic Theory of Metacognition in Minds and Machines—0
Analog Circuit Design with Dyna-Style Reinforcement Learning—0
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient—0
Analysing Congestion Problems in Multi-agent Reinforcement Learning—0
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation—0
Analysis and Improvement of Policy Gradient Estimation—0
Analysis of Agent Expertise in Ms. Pac-Man using Value-of-Information-based Policies—0
Analysis of Evolutionary Behavior in Self-Learning Media Search Engines—0
Analysis of Information Propagation in Ethereum Network Using Combined Graph Attention Network and Reinforcement Learning to Optimize Network Efficiency and Scalability—0
Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks—0
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Benchmark Results

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