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 3991–4000 of 15113 papers

TitleStatusHype
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
Deterministic Value-Policy Gradients—0
DETERRENT: Detecting Trojans using Reinforcement Learning—0
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning—0
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning—0
Developing cooperative policies for multi-stage reinforcement learning tasks—0
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments—0
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning—0
Domain Adaptation for Reinforcement Learning on the Atari—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
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Benchmark Results

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