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 4221–4230 of 15113 papers

TitleStatusHype
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic SpacesCode0
Align Your Intents: Offline Imitation Learning via Optimal Transport—0
Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning—0
Deep Hedging with Market Impact—0
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning—0
Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated PoliciesCode0
Offline Multi-task Transfer RL with Representational Penalization—0
Programmatic Reinforcement Learning: Navigating Gridworlds—0
Self-evolving Autoencoder Embedded Q-Network—0
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks—0
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Benchmark Results

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