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 2531–2540 of 15113 papers

TitleStatusHype
Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks—0
Average-Reward Learning and Planning with Options—0
AMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control—0
Average Reward Adjusted Discounted Reinforcement Learning: Near-Blackwell-Optimal Policies for Real-World Applications—0
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning—0
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference—0
A Benchmark for Low-Switching-Cost Reinforcement Learning—0
Controlling earthquake-like instabilities using artificial intelligence—0
Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation—0
Average Cost Optimal Control of Stochastic Systems Using Reinforcement Learning—0
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Benchmark Results

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