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

Showing 1070110750 of 15113 papers

TitleStatusHype
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading0
Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity0
FORM: Learning Expressive and Transferable First-Order Logic Reward Machines0
That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities0
The act of remembering: a study in partially observable reinforcement learning0
The Advantage Regret-Matching Actor-Critic0
The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI0
The Architectural Implications of Distributed Reinforcement Learning on CPU-GPU Systems0
The association problem in wireless networks: a Policy Gradient Reinforcement Learning approach0
The Bandit Whisperer: Communication Learning for Restless Bandits0
The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches0
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond0
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach0
The Case for Automatic Database Administration using Deep Reinforcement Learning0
The Central Role of the Loss Function in Reinforcement Learning0
The Challenges of Exploration for Offline Reinforcement Learning0
The Complexity of Markov Equilibrium in Stochastic Games0
The Complex Negotiation Dialogue Game0
The Concept of Criticality in Reinforcement Learning0
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning0
The Crucial Role of Problem Formulation in Real-World Reinforcement Learning0
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model0
The Differences Between Direct Alignment Algorithms are a Blur0
The Difficulty of Passive Learning in Deep Reinforcement Learning0
The Ecosystem Path to General AI0
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning0
Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features Learning Efficacy0
The effects of negative adaptation in Model-Agnostic Meta-Learning0
The Eigenoption-Critic Framework0
The Emergence of Individuality in Multi-Agent Reinforcement Learning0
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning0
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits0
The Essential Elements of Offline RL via Supervised Learning0
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey0
The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning0
The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning0
The Fallacy of Minimizing Cumulative Regret in the Sequential Task Setting0
The False Dawn: Reevaluating Google's Reinforcement Learning for Chip Macro Placement0
The Feasibility of Constrained Reinforcement Learning Algorithms: A Tutorial Study0
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents0
The Gambler's Problem and Beyond0
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint0
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication0
The Greatest Teacher, Failure is: Using Reinforcement Learning for SFC Placement Based on Availability and Energy Consumption0
The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning0
The Hierarchical Adaptive Forgetting Variational Filter0
The Immersion of Directed Multi-graphs in Embedding Fields. Generalisations0
Missing Velocity in Dynamic Obstacle Avoidance based on Deep Reinforcement Learning0
The impact of moving expenses on social segregation: a simulation with RL and ABM0
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning0
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Benchmark Results

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