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 8551–8575 of 15113 papers

TitleStatusHype
Integrating process design and control using reinforcement learning—0
Low-level Pose Control of Tilting Multirotor for Wall Perching Tasks Using Reinforcement Learning—0
An Approach to Partial Observability in Games: Learning to Both Act and Observe—0
Does Explicit Prediction Matter in Deep Reinforcement Learning-Based Energy Management?—0
Fairness Through Counterfactual UtilitiesCode0
Gap-Dependent Unsupervised Exploration for Reinforcement LearningCode0
Truncated Emphatic Temporal Difference Methods for Prediction and Control—0
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey—0
Imitation Learning by Reinforcement LearningCode0
High Quality Related Search Query Suggestions using Deep Reinforcement Learning—0
A Survey on Deep Reinforcement Learning for Data Processing and Analytics—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
Knowledge accumulating: The general pattern of learning—0
Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning—0
On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization—0
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning—0
Meta-Reinforcement Learning in Broad and Non-Parametric EnvironmentsCode0
Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents—0
Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning—0
A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning—0
Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications—0
Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning—0
On the Robustness of Controlled Deep Reinforcement Learning for Slice Placement—0
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach—0
Responding to Illegal Activities Along the Canadian Coastlines Using Reinforcement Learning—0
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Benchmark Results

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