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 10026–10050 of 15113 papers

TitleStatusHype
Chrome Dino Run using Reinforcement Learning—0
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving—0
Cautious Adaptation For Reinforcement Learning in Safety-Critical SettingsCode1
Explainability in Deep Reinforcement Learning—0
Safe Reinforcement Learning in Constrained Markov Decision ProcessesCode1
Reinforcement Learning with Quantum Variational CircuitsCode0
Model-Free Optimal Control of Linear Multi-Agent Systems via Decomposition and Hierarchical Approximation—0
OR-Gym: A Reinforcement Learning Library for Operations Research ProblemsCode1
Sample-efficient Cross-Entropy Method for Real-time PlanningCode1
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning—0
Adversary Agnostic Robust Deep Reinforcement Learning—0
Multi-Agent Deep Reinforcement Learning enabled Computation Resource Allocation in a Vehicular Cloud Network—0
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter—0
Robust Image Matching By Dynamic Feature Selection—0
Offline Meta-Reinforcement Learning with Advantage WeightingCode1
Reinforcement Learning with Trajectory Feedback—0
A Review of Deep Reinforcement Learning for Smart Building Energy Management—0
An ocular biomechanics environment for reinforcement learning—0
Overcoming Model Bias for Robust Offline Deep Reinforcement Learning—0
Model-Based Offline Planning—0
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey—0
An Intelligent Control Strategy for buck DC-DC Converter via Deep Reinforcement Learning—0
Batch Value-function Approximation with Only RealizabilityCode0
Comparison of Model Predictive and Reinforcement Learning Methods for Fault Tolerant Control—0
Fault-Tolerant Control of Degrading Systems with On-Policy Reinforcement Learning—0
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Benchmark Results

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