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 6451–6475 of 15113 papers

TitleStatusHype
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
Discovering Exfiltration Paths Using Reinforcement Learning with Attack Graphs—0
Discovering Generalizable Skills via Automated Generation of Diverse Tasks—0
Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning—0
Discovering Latent States for Model Learning: Applying Sensorimotor Contingencies Theory and Predictive Processing to Model Context—0
Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning—0
Discovering Options for Exploration by Minimizing Cover Time—0
Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning—0
Discover the Hidden Attack Path in Multi-domain Cyberspace Based on Reinforcement Learning—0
Discovery of False Data Injection Schemes on Frequency Controllers with Reinforcement Learning—0
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Benchmark Results

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