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 9926–9950 of 15113 papers

TitleStatusHype
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning—0
DINASTI: Dialogues with a Negotiating Appointment Setting Interface—0
DIP-R1: Deep Inspection and Perception with RL Looking Through and Understanding Complex Scenes—0
DIP-RL: Demonstration-Inferred Preference Learning in Minecraft—0
Direct and indirect reinforcement learning—0
Directed Exploration for Reinforcement Learning—0
Directed Exploration in PAC Model-Free Reinforcement Learning—0
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice—0
Revisiting a Design Choice in Gradient Temporal Difference Learning—0
Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods—0
Direct Mutation and Crossover in Genetic Algorithms Applied to Reinforcement Learning Tasks—0
Direct optimization of F-measure for retrieval-based personal question answering—0
Direct Uncertainty Estimation in Reinforcement Learning—0
Dirichlet policies for reinforced factor portfolios—0
Discerning Temporal Difference Learning—0
DisCoRL: Continual Reinforcement Learning via Policy Distillation—0
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies—0
Discounted Reinforcement Learning Is Not an Optimization Problem—0
Discourse-Aware Neural Rewards for Coherent Text Generation—0
Discourse Coherence, Reference Grounding and Goal Oriented Dialogue—0
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning—0
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning—0
Discovering Blind Spots in Reinforcement Learning—0
Discovering Command and Control (C2) Channels on Tor and Public Networks Using Reinforcement Learning—0
Discovering Command and Control Channels Using Reinforcement Learning—0
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Benchmark Results

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