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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6051–6075 of 15113 papers

TitleStatusHype
Learning Time-Invariant Reward Functions through Model-Based Inverse Reinforcement Learning—0
Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applications—0
Learning to act: a Reinforcement Learning approach to recommend the best next activities—0
Learning to Act in Decentralized Partially Observable MDPs—0
Learning to Activate Relay Nodes: Deep Reinforcement Learning Approach—0
Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning—0
Learning to Assign: Towards Fair Task Assignment in Large-Scale Ride Hailing—0
Learning to Assist Agents by Observing Them—0
Learning to be Safe: Deep RL with a Safety Critic—0
Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning—0
Learning to Centralize Dual-Arm Assembly—0
Learning to Clarify by Reinforcement Learning Through Reward-Weighted Fine-Tuning—0
Learning to Code: Coded Caching via Deep Reinforcement Learning—0
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication—0
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning—0
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning—0
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems—0
Learning to Communicate in Multi-Agent Reinforcement Learning : A Review—0
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning—0
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks—0
Learning to Communicate with Intent: An Introduction—0
Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System—0
Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation—0
Learning to Compose Words into Sentences with Reinforcement Learning—0
Learning to Control DC Motor for Micromobility in Real Time with Reinforcement Learning—0
Show:102550
← PrevPage 243 of 605Next →

Benchmark Results

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