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 6501–6525 of 15113 papers

TitleStatusHype
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
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning—0
The Importance of Constraint Smoothness for Parameter Estimation in Computational Cognitive Modeling—0
The Importance of Credo in Multiagent Learning—0
The Importance of Sampling inMeta-Reinforcement Learning—0
The Ingredients of Real World Robotic Reinforcement Learning—0
The Ingredients of Real-World Robotic Reinforcement Learning—0
The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study—0
The Ladder in Chaos: A Simple and Effective Improvement to General DRL Algorithms by Policy Path Trimming and Boosting—0
The Laplacian in RL: Learning Representations with Efficient Approximations—0
The Least Restriction for Offline Reinforcement Learning—0
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure—0
The Logical Options Framework—0
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Benchmark Results

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