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 3461–3470 of 15113 papers

TitleStatusHype
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation—0
Counterfactual Explanation Policies in RL—0
A Strong Baseline for Batch Imitation Learning—0
Deep Occupancy-Predictive Representations for Autonomous Driving—0
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications—0
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning—0
Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation—0
Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning—0
Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting—0
Counterfactual Credit Assignment in Model-Free Reinforcement Learning—0
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Benchmark Results

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