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 4481–4490 of 15113 papers

TitleStatusHype
Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems—0
SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering—0
Meta-Reinforcement Learning Using Model Parameters—0
Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion—0
ERL-Re^2: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy RepresentationCode1
Knowledge-Guided Exploration in Deep Reinforcement Learning—0
Environment Design for Inverse Reinforcement LearningCode0
Low-Rank Modular Reinforcement Learning via Muscle SynergyCode1
Quantum deep recurrent reinforcement learning—0
Provable Safe Reinforcement Learning with Binary FeedbackCode1
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Benchmark Results

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