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 9326–9350 of 15113 papers

TitleStatusHype
An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning—0
Full Gradient DQN Reinforcement Learning: A Provably Convergent Scheme—0
WFA-IRL: Inverse Reinforcement Learning of Autonomous Behaviors Encoded as Weighted Finite Automata—0
Learning to Infer Unseen Contexts in Causal Contextual Reinforcement Learning—0
Automatic Goal Generation using Dynamical Distance Learning—0
A Learning-Based Computational Impact Time Guidance—0
Learning to Explore a Class of Multiple Reward-Free Environments—0
Learning Task Informed Abstractions—0
I am Robot: Neuromuscular Reinforcement Learning to Actuate Human Limbs through Functional Electrical Stimulation—0
Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning—0
Challenges for Reinforcement Learning in Healthcare—0
Less Suboptimal Learning and Control in Variational POMDPs—0
Learning State Representations via Temporal Cycle-Consistency Constraint in Model-Based Reinforcement Learning—0
LOCO: Adaptive exploration in reinforcement learning via local estimation of contraction coefficients—0
A Scavenger Hunt for Service RobotsCode0
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning—0
Pretraining Reward-Free Representations for Data-Efficient Reinforcement Learning—0
Parametrized quantum policies for reinforcement learning—0
Solipsistic Reinforcement Learning—0
Resolving Causal Confusion in Reinforcement Learning via Robust Exploration—0
Minimum Description Length Skills for Accelerated Reinforcement Learning—0
Out-of-distribution generalization of internal models is correlated with reward—0
Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning—0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation—0
Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning—0
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Benchmark Results

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