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 4776–4800 of 15113 papers

TitleStatusHype
Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review—0
Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network—0
Appraisal-Guided Proximal Policy Optimization: Modeling Psychological Disorders in Dynamic Grid World—0
Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods—0
Approximate discounting-free policy evaluation from transient and recurrent states—0
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning—0
Approximate Dynamic Programming For Linear Systems with State and Input Constraints—0
Approximate Equivariance in Reinforcement Learning—0
A policy gradient approach for optimization of smooth risk measures—0
Approximate information state based convergence analysis of recurrent Q-learning—0
Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning—0
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning—0
Approximated Multi-Agent Fitted Q Iteration—0
Does DQN Learn?—0
Approximate Robust NMPC using Reinforcement Learning—0
Approximating a deep reinforcement learning docking agent using linear model trees—0
Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning—0
Approximating Euclidean by Imprecise Markov Decision Processes—0
Approximating Martingale Process for Variance Reduction in Deep Reinforcement Learning with Large State Space—0
Approximating meta-heuristics with homotopic recurrent neural networks—0
Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning—0
A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems—0
A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning—0
APRIL: Active Preference-learning based Reinforcement Learning—0
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee—0
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Benchmark Results

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