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 8176–8200 of 15113 papers

TitleStatusHype
Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies—0
Particle Value Functions—0
Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks—0
Partner Approximating Learners (PAL): Simulation-Accelerated Learning with Explicit Partner Modeling in Multi-Agent Domains—0
Partner Personas Generation for Dialogue Response Generation—0
PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning—0
Passing Through Narrow Gaps with Deep Reinforcement Learning—0
Pass@K Policy Optimization: Solving Harder Reinforcement Learning Problems—0
Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots—0
Pathfinding in Random Partially Observable Environments with Vision-Informed Deep Reinforcement Learning—0
Path Following and Stabilisation of a Bicycle Model using a Reinforcement Learning Approach—0
Path Integral Networks: End-to-End Differentiable Optimal Control—0
Machine learning strategies for path-planning microswimmers in turbulent flows—0
Path Planning of Cleaning Robot with Reinforcement Learning—0
Path Planning using Reinforcement Learning: A Policy Iteration Approach—0
Patient level simulation and reinforcement learning to discover novel strategies for treating ovarian cancer—0
Patterns, predictions, and actions: A story about machine learning—0
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching—0
Pauli Network Circuit Synthesis with Reinforcement Learning—0
Paused Agent Replay Refresh—0
Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making—0
PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning—0
PDQN - A Deep Reinforcement Learning Method for Planning with Long Delays: Optimization of Manufacturing Dispatching—0
PEARL: Parallelized Expert-Assisted Reinforcement Learning for Scene Rearrangement Planning—0
PEAR: Primitive enabled Adaptive Relabeling for boosting Hierarchical Reinforcement Learning—0
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Benchmark Results

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