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 4511–4520 of 15113 papers

TitleStatusHype
Hardness in Markov Decision Processes: Theory and Practice—0
ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement LearningCode1
Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement LearningCode0
AACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Experience ReplayCode0
Avalon: A Benchmark for RL Generalization Using Procedurally Generated WorldsCode1
Dichotomy of Control: Separating What You Can Control from What You Cannot—0
Evaluating Long-Term Memory in 3D MazesCode1
Multi-Agent Path Finding via Tree LSTMCode1
Reachability-Aware Laplacian Representation in Reinforcement Learning—0
Climate Change Policy Exploration using Reinforcement Learning—0
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Benchmark Results

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