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

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Showing 8801–8825 of 15113 papers

TitleStatusHype
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
Pedestrian Prediction by Planning using Deep Neural Networks—0
Penalized Proximal Policy Optimization for Safe Reinforcement Learning—0
PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making—0
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey—0
Perception-Prediction-Reaction Agents for Deep Reinforcement Learning—0
Perceptual Reward Functions—0
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Benchmark Results

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