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 6826–6850 of 15113 papers

TitleStatusHype
Trajectory Modeling via Random Utility Inverse Reinforcement Learning—0
Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning—0
Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces—0
Trajectory representation learning for Multi-Task NMRDPs planning—0
Trajectory Tracking of Underactuated Sea Vessels With Uncertain Dynamics: An Integral Reinforcement Learning Approach—0
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding—0
TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving—0
TransDreamer: Reinforcement Learning with Transformer World Models—0
Transferable Curricula through Difficulty Conditioned Generators—0
Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications—0
Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots—0
Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents—0
Distributional Successor Features Enable Zero-Shot Policy Optimization—0
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
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Benchmark Results

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