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 3991–4000 of 15113 papers

TitleStatusHype
Decentralized model-free reinforcement learning in stochastic games with average-reward objective—0
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity—0
A Constrained-Optimization Approach to the Execution of Prioritized Stacks of Learned Multi-Robot Tasks—0
Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior—0
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning—0
Mutation Testing of Deep Reinforcement Learning Based on Real FaultsCode0
Safe Policy Improvement for POMDPs via Finite-State Controllers—0
Reinforcement Learning-based Joint Handover and Beam Tracking in Millimeter-wave Networks—0
Predictive World Models from Real-World Partial ObservationsCode0
Asynchronous training of quantum reinforcement learning—0
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Benchmark Results

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