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 3931–3940 of 15113 papers

TitleStatusHype
Assessing Transferability from Simulation to Reality for Reinforcement Learning—0
De Novo Molecular Design Enabled by Direct Preference Optimization and Curriculum Learning—0
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning—0
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations—0
Coordination-driven learning in multi-agent problem spaces—0
Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework—0
Density Constrained Reinforcement Learning—0
Basic protocols in quantum reinforcement learning with superconducting circuits—0
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL—0
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel—0
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Benchmark Results

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