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 11376–11400 of 15113 papers

TitleStatusHype
Learning State Abstractions for Transfer in Continuous ControlCode0
A data-driven choice of misfit function for FWI using reinforcement learning—0
Analyzing Policy Distillation on Multi-Task Learning and Meta-Reinforcement Learning in Meta-World—0
Description Based Text Classification with Reinforcement Learning—0
Causally Correct Partial Models for Reinforcement Learning—0
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation—0
Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning—0
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning—0
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts—0
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces—0
Manipulating Reinforcement Learning: Poisoning Attacks on Cost Signals—0
Reward-Free Exploration for Reinforcement Learning—0
Student/Teacher Advising through Reward Augmentation—0
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting—0
Temporal-adaptive Hierarchical Reinforcement Learning—0
Social diversity and social preferences in mixed-motive reinforcement learning—0
Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning—0
Deep Radial-Basis Value Functions for Continuous Control—0
Learning Task-Driven Control Policies via Information Bottlenecks—0
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise—0
Bootstrapping a DQN Replay Memory with Synthetic Experiences—0
Policy Gradient based Quantum Approximate Optimization Algorithm—0
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes—0
Evolutionary algorithms for constructing an ensemble of decision trees—0
Deep Reinforcement Learning for Autonomous Driving: A Survey—0
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Benchmark Results

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