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 2926–2950 of 15113 papers

TitleStatusHype
Influence-Based Multi-Agent ExplorationCode0
Impartial Games: A Challenge for Reinforcement LearningCode0
Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog ManagementCode0
Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial TrainingCode0
Controlling epidemics through optimal allocation of test kits and vaccine doses across networksCode0
Imitation Learning by Reinforcement LearningCode0
Quantum enhancements for deep reinforcement learning in large spacesCode0
Input Convex Neural NetworksCode0
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learningCode0
Continuous-action Reinforcement Learning for Playing Racing Games: Comparing SPG to PPOCode0
Continual Task Learning through Adaptive Policy Self-CompositionCode0
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement LearningCode0
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement LearningCode0
Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement LearningCode0
Importance Prioritized Policy DistillationCode0
Imagination-Augmented Agents for Deep Reinforcement LearningCode0
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level GenerationCode0
IGN : Implicit Generative NetworksCode0
Continual Reinforcement Learning in 3D Non-stationary EnvironmentsCode0
Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer LearningCode0
A Framework for Automated Cellular Network Tuning with Reinforcement LearningCode0
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation TasksCode0
Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned PoliciesCode0
Continual Policy Distillation of Reinforcement Learning-based Controllers for Soft Robotic In-Hand ManipulationCode0
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Benchmark Results

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