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 1191–1200 of 15113 papers

TitleStatusHype
Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning—0
Swarm Behavior Cloning—0
ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement TasksCode2
Skill-Enhanced Reinforcement Learning Acceleration from Demonstrations—0
Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone—0
Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation—0
Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study—0
Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application—0
M^3PC: Test-time Model Predictive Control for Pretrained Masked Trajectory ModelCode1
Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories—0
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Benchmark Results

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