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 4311–4320 of 15113 papers

TitleStatusHype
A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning—0
A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem—0
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms—0
A Distributional View on Multi-Objective Policy Optimization—0
A distributional view on multi objective policy optimization—0
A Distribution-Aware Flow-Matching for Generating Unstructured Data for Few-Shot Reinforcement Learning—0
Adjacency constraint for efficient hierarchical reinforcement learning—0
Accelerating Distributed Deep Reinforcement Learning by In-Network Experience Sampling—0
A Dual-Agent Adversarial Framework for Robust Generalization in Deep Reinforcement Learning—0
A Dual-Critic Reinforcement Learning Framework for Frame-level Bit Allocation in HEVC/H.265—0
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Benchmark Results

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