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 4461–4470 of 15113 papers

TitleStatusHype
Agent-Controller Representations: Principled Offline RL with Rich Exogenous InformationCode1
Disentangled (Un)Controllable FeaturesCode0
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment TreesCode1
Teacher-student curriculum learning for reinforcement learning—0
Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement LearningCode0
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning—0
DanZero: Mastering GuanDan Game with Reinforcement Learning—0
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games—0
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning—0
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction—0
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Benchmark Results

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