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 10851–10875 of 15113 papers

TitleStatusHype
Action Space Shaping in Deep Reinforcement LearningCode1
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement LearningCode0
Exploration of Reinforcement Learning for Event Camera using Car-like Robots—0
Value Driven Representation for Human-in-the-Loop Reinforcement Learning—0
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?—0
Constrained-Space Optimization and Reinforcement Learning for Complex Tasks—0
Learning Sparse Rewarded Tasks from Sub-Optimal DemonstrationsCode0
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication—0
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning—0
Mimicking Evolution with Reinforcement Learning—0
Augmented Q Imitation Learning (AQIL)Code0
Exploration in Action SpaceCode0
Learning to Ask Medical Questions using Reinforcement LearningCode0
Controlling Rayleigh-Bénard convection via Reinforcement Learning—0
Leverage the Average: an Analysis of KL Regularization in RL—0
Robotic Table Tennis with Model-Free Reinforcement Learning—0
Optimal Bidding Strategy without Exploration in Real-time Bidding—0
Optimising Lockdown Policies for Epidemic Control using Reinforcement LearningCode0
Straight to the Point: Fast-forwarding Videos via Reinforcement Learning Using Textual DataCode0
Ultrasound-Guided Robotic Navigation with Deep Reinforcement LearningCode1
Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties—0
Agent57: Outperforming the Atari Human BenchmarkCode1
Deep reinforcement learning for large-scale epidemic controlCode1
Multi-Task Reinforcement Learning with Soft ModularizationCode1
Suphx: Mastering Mahjong with Deep Reinforcement LearningCode0
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Benchmark Results

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