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 3261–3270 of 15113 papers

TitleStatusHype
From Laws to Motivation: Guiding Exploration through Law-Based Reasoning and Rewards—0
Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration—0
Enhancing Molecular Design through Graph-based Topological Reinforcement Learning—0
Segmenting Action-Value Functions Over Time-Scales in SARSA via TD(Δ)—0
Free Energy Projective Simulation (FEPS): Active inference with interpretability—0
Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward—0
Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problemsCode0
Time-Scale Separation in Q-Learning: Extending TD() for Action-Value Function Decomposition—0
Model Checking for Reinforcement Learning in Autonomous Driving: One Can Do More Than You Think!—0
GraCo -- A Graph Composer for Integrated Circuits—0
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Benchmark Results

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