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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4201–4225 of 15113 papers

TitleStatusHype
Adaptive patch foraging in deep reinforcement learning agents—0
Adaptive perturbation adversarial training: based on reinforcement learning—0
Adaptive Policy Learning for Offline-to-Online Reinforcement Learning—0
Adaptive Policy Transfer in Reinforcement Learning—0
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference—0
Adaptive Q-learning for Interaction-Limited Reinforcement Learning—0
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning—0
Adaptive Reinforcement Learning for Unobservable Random Delays—0
Adaptive Reinforcement Learning for State Avoidance in Discrete Event Systems—0
Adaptive Reinforcement Learning Model for Simulation of Urban Mobility during Crises—0
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks—0
Adaptive Reward-Poisoning Attacks against Reinforcement Learning—0
Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning—0
Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL—0
Adaptive routing protocols for determining optimal paths in AI multi-agent systems: a priority- and learning-enhanced approach—0
Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols—0
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization—0
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization—0
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning—0
Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning—0
Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning—0
Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT—0
Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control—0
Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning—0
Adaptive Stress Testing for Adversarial Learning in a Financial Environment—0
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Benchmark Results

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