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 2551–2575 of 15113 papers

TitleStatusHype
A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers—0
Adaptive Policy Learning for Offline-to-Online Reinforcement Learning—0
Autotuning PID control using Actor-Critic Deep Reinforcement Learning—0
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning—0
A method for the online construction of the set of states of a Markov Decision Process using Answer Set Programming—0
Autotelic Reinforcement Learning: Exploring Intrinsic Motivations for Skill Acquisition in Open-Ended Environments—0
A Method for Fast Autonomy Transfer in Reinforcement Learning—0
Adaptive perturbation adversarial training: based on reinforcement learning—0
Conversational Question Answering with Reformulations over Knowledge Graph—0
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications—0
A Meta-Reinforcement Learning Approach to Process Control—0
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming—0
Autoregressive Multi-trait Essay Scoring via Reinforcement Learning with Scoring-aware Multiple Rewards—0
Adaptive patch foraging in deep reinforcement learning agents—0
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER—0
Convergent and Efficient Deep Q Learning Algorithm—0
Autonomous Warehouse Robot using Deep Q-Learning—0
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles—0
myGym: Modular Toolkit for Visuomotor Robotic Tasks—0
Convergent NMPC-based Reinforcement Learning Using Deep Expected Sarsa and Nonlinear Temporal Difference Learning—0
Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning—0
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning—0
Task-Agnostic Learning to Accomplish New Tasks—0
Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review—0
Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach—0
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Benchmark Results

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