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 8751–8775 of 15113 papers

TitleStatusHype
Privacy-Preserving Kickstarting Deep Reinforcement Learning with Privacy-Aware Learners—0
Reinforcement Learning for Datacenter Congestion Control—0
Strategic bidding in freight transport using deep reinforcement learning—0
Learning Memory-Dependent Continuous Control from Demonstrations—0
Continuous Doubly Constrained Batch Reinforcement LearningCode0
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning—0
Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning—0
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method—0
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning—0
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs—0
TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution—0
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments—0
Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents—0
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models—0
Transferring Domain Knowledge with an Adviser in Continuous Tasks—0
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—0
Training Larger Networks for Deep Reinforcement Learning—0
Improper Reinforcement Learning with Gradient-based Policy Optimization—0
IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning—0
Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary—0
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees—0
Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport—0
Learning from Demonstrations using Signal Temporal Logic—0
Developing parsimonious ensembles using predictor diversity within a reinforcement learning frameworkCode0
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Benchmark Results

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