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 4111–4120 of 15113 papers

TitleStatusHype
Critic-Guided Decoding for Controlled Text GenerationCode1
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios—0
Hyperparameters in Contextual RL are Highly SituationalCode0
A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling—0
Lifelong Reinforcement Learning with Modulating MasksCode0
Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement LearningCode0
Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning—0
Variational Quantum Soft Actor-Critic for Robotic Arm Control—0
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons—0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning—0
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Benchmark Results

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