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 11076–11100 of 15113 papers

TitleStatusHype
Transfer among Agents: An Efficient Multiagent Transfer Learning Framework—0
Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle—0
Transfer from Multiple MDPs—0
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement—0
Transfer in Deep Reinforcement Learning using Knowledge Graphs—0
Transfer in Reinforcement Learning via Regret Bounds for Learning Agents—0
Transfer Learning and Curriculum Learning in Sokoban—0
Transfer Learning by Modeling a Distribution over Policies—0
Transfer Learning for Efficient Iterative Safety Validation—0
Transfer learning for process design with reinforcement learning—0
Transfer Learning in Deep Reinforcement Learning: A Survey—0
Transfer learning with causal counterfactual reasoning in Decision Transformers—0
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop—0
Transfer of Temporal Logic Formulas in Reinforcement Learning—0
Transfer of Value Functions via Variational Methods—0
Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition—0
Transferred Q-learning—0
Transfer Reinforcement Learning under Unobserved Contextual Information—0
Transferring Agent Behaviors from Videos via Motion GANs—0
Transferring Autonomous Driving Knowledge on Simulated and Real Intersections—0
Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation—0
Transferring Domain Knowledge with an Adviser in Continuous Tasks—0
Transferring Expectations in Model-based Reinforcement Learning—0
Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation—0
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem—0
Show:102550
← PrevPage 444 of 605Next →

Benchmark Results

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