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 6101–6125 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
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Benchmark Results

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