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 2961–2970 of 15113 papers

TitleStatusHype
A Tensor Network Approach to Finite Markov Decision Processes—0
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning—0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors—0
A Temporal Difference Reinforcement Learning Theory of Emotion: unifying emotion, cognition and adaptive behavior—0
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming—0
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations—0
ACDER: Augmented Curiosity-Driven Experience Replay—0
DECORE: Deep Compression with Reinforcement Learning—0
Decoupled Learning of Environment Characteristics for Safe Exploration—0
A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning—0
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Benchmark Results

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