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 1521–1530 of 15113 papers

TitleStatusHype
Agent57: Outperforming the Atari Human BenchmarkCode1
Mirror Descent Policy OptimizationCode1
Deep Transformer Q-Networks for Partially Observable Reinforcement LearningCode1
Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector QuantizationCode1
Basis for Intentions: Efficient Inverse Reinforcement Learning using Past ExperienceCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
Fully Decentralized Multi-Agent Reinforcement Learning with Networked AgentsCode1
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic RewardsCode1
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive EnvironmentsCode1
A Deep Reinforced Model for Abstractive SummarizationCode1
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Benchmark Results

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