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 4451–4460 of 15113 papers

TitleStatusHype
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman OperatorCode0
Efficient Parallel Reinforcement Learning Framework using the Reactor ModelCode0
Safety-Enhanced Self-Learning for Optimal Power Converter Control—0
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer—0
Language Model Alignment with Elastic ResetCode0
Evaluation of Active Feature Acquisition Methods for Static Feature Settings—0
Diffused Task-Agnostic Milestone Planner—0
Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks—0
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications—0
LExCI: A Framework for Reinforcement Learning with Embedded SystemsCode0
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Benchmark Results

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