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 6101–6150 of 15113 papers

TitleStatusHype
Learning to Grasp the Ungraspable with Emergent Extrinsic Dexterity—0
Learning to grow: control of material self-assembly using evolutionary reinforcement learning—0
Learning to Guide a Saturation-Based Theorem Prover—0
Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II—0
Learning to Herd Agents Amongst Obstacles: Training Robust Shepherding Behaviors using Deep Reinforcement Learning—0
Learning to Infer Unseen Contexts in Causal Contextual Reinforcement Learning—0
Learning to Influence Human Behavior with Offline Reinforcement Learning—0
Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration—0
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning—0
Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learning—0
Learning to Locomote with Deep Neural-Network and CPG-based Control in a Soft Snake Robot—0
Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting—0
Learning to Mitigate AI Collusion on Economic Platforms—0
Learning to Mix n-Step Returns: Generalizing lambda-Returns for Deep Reinforcement Learning—0
Learning to Navigate the Web—0
Learning to Observe with Reinforcement Learning—0
Learning to Operate an Electric Vehicle Charging Station Considering Vehicle-grid Integration—0
Learning to Operate in Open Worlds by Adapting Planning Models—0
Learning to Optimize—0
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads—0
Learning to Optimize Neural Nets—0
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning—0
Learning to Order Sub-questions for Complex Question Answering—0
Learning to Perform Physics Experiments via Deep Reinforcement Learning—0
Learning to Plan via Deep Optimistic Value Exploration—0
Learning to Play Pong using Policy Gradient Learning—0
Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World—0
Learning to Play Table Tennis From Scratch using Muscular Robots—0
Learning to Play Two-Player Perfect-Information Games without Knowledge—0
Learning to predict where to look in interactive environments using deep recurrent q-learning—0
Learning to Program Variational Quantum Circuits with Fast Weights—0
Learning to Progressively Plan—0
Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems—0
Learning to Prune Deep Neural Networks via Reinforcement Learning—0
Learning to Query Internet Text for Informing Reinforcement Learning Agents—0
Learning to Reach Goals Without Reinforcement Learning—0
Learning to Reason: Distilling Hierarchy via Self-Supervision and Reinforcement Learning—0
Learning to Reason in Large Theories without Imitation—0
Learning to Recover Sparse Signals—0
Learning to Reinforcement Learn by Imitation—0
Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning—0
Learning to Represent Action Values as a Hypergraph on the Action Vertices—0
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games—0
Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning—0
Learning Torque Control for Quadrupedal Locomotion—0
Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning—0
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data—0
Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments—0
Learning to sample in Cartesian MRI—0
Learning to Sample with Local and Global Contexts in Experience Replay Buffer—0
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Benchmark Results

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