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 3191–3200 of 15113 papers

TitleStatusHype
D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning—0
A Survey on Traffic Signal Control Methods—0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning—0
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Agent Probing Interaction Policies—0
Credit-cognisant reinforcement learning for multi-agent cooperation—0
DanZero: Mastering GuanDan Game with Reinforcement Learning—0
DAQN: Deep Auto-encoder and Q-Network—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
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Benchmark Results

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