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 976–1000 of 15113 papers

TitleStatusHype
Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy OptimizationCode1
Deep Intrinsically Motivated Exploration in Continuous ControlCode1
Does Zero-Shot Reinforcement Learning Exist?Code1
Offline Reinforcement Learning via High-Fidelity Generative Behavior ModelingCode1
A simple but strong baseline for online continual learning: Repeated Augmented RehearsalCode1
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion PlanningCode1
Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward EnvironmentsCode1
End-to-End Affordance Learning for Robotic ManipulationCode1
Training Efficient Controllers via Analytic Policy GradientCode1
Mastering the Unsupervised Reinforcement Learning Benchmark from PixelsCode1
LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement LearningCode1
Revisiting Discrete Soft Actor-CriticCode1
Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic EnvironmentsCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
MAN: Multi-Action Networks LearningCode1
Latent Plans for Task-Agnostic Offline Reinforcement LearningCode1
Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement LearningCode1
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless NetworksCode1
Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage ControlCode1
Model-based gym environments for limit order book tradingCode1
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model CheckingCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Model-based Reinforcement Learning with Multi-step Plan Value EstimationCode1
Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest OverfittingCode1
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Benchmark Results

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