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 4521–4530 of 15113 papers

TitleStatusHype
A Local Temporal Difference Code for Distributional Reinforcement Learning—0
A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning—0
AlphaD3M: Machine Learning Pipeline Synthesis—0
Alpha-DAG: a reinforcement learning based algorithm to learn Directed Acyclic Graphs—0
Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation—0
AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search—0
AlphaSeq: Sequence Discovery with Deep Reinforcement Learning—0
AlphaSnake: Policy Iteration on a Nondeterministic NP-hard Markov Decision Process—0
AlphaStar: An Evolutionary Computation Perspective—0
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks—0
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Benchmark Results

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