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 9251–9275 of 15113 papers

TitleStatusHype
Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments—0
Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach—0
Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking—0
Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopes—0
Provably Sample-Efficient RL with Side Information about Latent Dynamics—0
Proximal Bellman mappings for reinforcement learning and their application to robust adaptive filtering—0
Proximal Deterministic Policy Gradient—0
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning—0
Proximal Policy Optimization and its Dynamic Version for Sequence Generation—0
Proximal Policy Optimization-Based Reinforcement Learning Approach for DC-DC Boost Converter Control: A Comparative Evaluation Against Traditional Control Techniques—0
Proximal Policy Optimization for Tracking Control Exploiting Future Reference Information—0
Proximal Policy Optimization via Enhanced Exploration Efficiency—0
Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces—0
Proximal Reliability Optimization for Reinforcement Learning—0
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning—0
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy—0
Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control—0
PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets—0
Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care—0
Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning—0
Pseudorehearsal in actor-critic agents—0
Pseudorehearsal in actor-critic agents with neural network function approximation—0
Pseudorehearsal in value function approximation—0
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning—0
Reinforcement Learning and its Connections with Neuroscience and Psychology—0
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Benchmark Results

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