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 6626–6650 of 15113 papers

TitleStatusHype
Dominion: A New Frontier for AI Research—0
Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured Multi-Turn Decomposition—0
Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning—0
Don't do it: Safer Reinforcement Learning With Rule-based Guidance—0
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL—0
Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework—0
Don't Get Yourself into Trouble! Risk-aware Decision-Making for Autonomous Vehicles—0
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning—0
Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation—0
DOOM: A Novel Adversarial-DRL-Based Op-Code Level Metamorphic Malware Obfuscator for the Enhancement of IDS—0
DOP: Deep Optimistic Planning with Approximate Value Function Evaluation—0
Do recent advancements in model-based deep reinforcement learning really improve data efficiency?—0
Importance of using appropriate baselines for evaluation of data-efficiency in deep reinforcement learning for Atari—0
Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation—0
Double A3C: Deep Reinforcement Learning on OpenAI Gym Games—0
Double Deep Q Networks for Sensor Management in Space Situational Awareness—0
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments—0
Double Q(σ) and Q(σ, λ): Unifying Reinforcement Learning Control Algorithms—0
Double Q-learning—0
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation—0
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning—0
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning—0
DPO: A Differential and Pointwise Control Approach to Reinforcement Learning—0
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System—0
DQNAS: Neural Architecture Search using Reinforcement Learning—0
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Benchmark Results

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