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 10726–10750 of 15113 papers

TitleStatusHype
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning—0
Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features Learning Efficacy—0
The effects of negative adaptation in Model-Agnostic Meta-Learning—0
The Eigenoption-Critic Framework—0
The Emergence of Individuality in Multi-Agent Reinforcement Learning—0
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning—0
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits—0
The Essential Elements of Offline RL via Supervised Learning—0
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey—0
The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning—0
The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning—0
The Fallacy of Minimizing Cumulative Regret in the Sequential Task Setting—0
The False Dawn: Reevaluating Google's Reinforcement Learning for Chip Macro Placement—0
The Feasibility of Constrained Reinforcement Learning Algorithms: A Tutorial Study—0
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents—0
The Gambler's Problem and Beyond—0
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint—0
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication—0
The Greatest Teacher, Failure is: Using Reinforcement Learning for SFC Placement Based on Availability and Energy Consumption—0
The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning—0
The Hierarchical Adaptive Forgetting Variational Filter—0
The Immersion of Directed Multi-graphs in Embedding Fields. Generalisations—0
Missing Velocity in Dynamic Obstacle Avoidance based on Deep Reinforcement Learning—0
The impact of moving expenses on social segregation: a simulation with RL and ABM—0
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning—0
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Benchmark Results

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