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
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning—0
A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)—0
GitFL: Adaptive Asynchronous Federated Learning using Version Control—0
UNSAT Solver Synthesis via Monte Carlo Forest SearchCode0
Safe Control and Learning Using the Generalized Action Governor—0
The impact of moving expenses on social segregation: a simulation with RL and ABM—0
A Reinforcement Learning Approach to Optimize Available Network Bandwidth Utilization—0
imitation: Clean Imitation Learning ImplementationsCode3
A Deep Reinforcement Learning Approach to Rare Event Estimation—0
PhysQ: A Physics Informed Reinforcement Learning Framework for Building Control—0
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Benchmark Results

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