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 9851–9875 of 15113 papers

TitleStatusHype
Designing Interpretable Approximations to Deep Reinforcement Learning—0
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach—0
Designing realistic RL environment for power systems—0
Designing Rewards for Fast Learning—0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning—0
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter—0
Design of Interacting Particle Systems for Fast Linear Quadratic RL—0
Design of Restricted Normalizing Flow towards Arbitrary Stochastic Policy with Computational Efficiency—0
Design Principles of the Hippocampal Cognitive Map—0
Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach—0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning—0
Detecting and Adapting to Novelty in Games—0
Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study—0
Detecting Deceptive Reviews using Generative Adversarial Networks—0
Detecting Worst-case Corruptions via Loss Landscape Curvature in Deep Reinforcement Learning—0
Deterministic Exploration via Stationary Bellman Error Maximization—0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Deterministic Value-Policy Gradients—0
DETERRENT: Detecting Trojans using Reinforcement Learning—0
Developing cooperative policies for multi-stage tasks—0
Developing cooperative policies for multi-stage reinforcement learning tasks—0
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments—0
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning—0
Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework—0
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Benchmark Results

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