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 4341–4350 of 15113 papers

TitleStatusHype
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
Adversarial Attacks on Deep Algorithmic Trading Policies—0
Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation—0
Adversarial Environment Design via Regret-Guided Diffusion Models—0
Adversarial Exploitation of Policy Imitation—0
Adversarial Feature Training for Generalizable Robotic Visuomotor Control—0
Adversarial Imitation Learning On Aggregated Data—0
Adversarial Imitation Learning via Random Search—0
Adversarial Imitation via Variational Inverse Reinforcement Learning—0
Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning—0
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Benchmark Results

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