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 2971–2980 of 15113 papers

TitleStatusHype
A Teacher-Student Framework for Maintainable Dialog Manager—0
A Taxonomy of Similarity Metrics for Markov Decision Processes—0
Adaptive ABAC Policy Learning: A Reinforcement Learning Approach—0
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics—0
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games—0
Atari games and Intel processors—0
Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning—0
Gamifying the Vehicle Routing Problem with Stochastic Requests—0
A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound—0
A Hierarchical Two-tier Approach to Hyper-parameter Optimization in Reinforcement Learning—0
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Benchmark Results

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