Co-occurring keywords · papers mentioning both
Papers
Logarithmic regret in communicating MDPs: Leveraging known dynamics with bandits
ACML 2023
Off-Policy Average Reward Actor-Critic with Deterministic Policy Search
ICML 2023
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
NeurIPS 2023
Robust Average-Reward Markov Decision Processes
AAAI 2023
Sharper Model-free Reinforcement Learning for Average-reward Markov Decision Processes
COLT 2023
A Provably-Efficient Model-Free Algorithm for Infinite-Horizon Average-Reward Constrained Markov Decision Processes
AAAI 2022
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward
ICLR 2022
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
NeurIPS 2022
Average-Reward Learning and Planning with Options
NeurIPS 2021
Optimal Policies Tend To Seek Power
NeurIPS 2021
Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks
AAAI 2021
Finite-Memory Near-Optimal Learning for Markov Decision Processes with Long-Run Average Reward
UAI 2020
Hierarchical Average Reward Policy Gradient Algorithms (Student Abstract)
AAAI 2020
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards
ICML 2020
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
ICML 2020
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward
NeurIPS 2020
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs
NeurIPS 2019
Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards
NeurIPS 2019
On Average Reward Policy Evaluation in Infinite-State Partially Observable Systems
AISTATS 2012
Adaptive Step-size Policy Gradients with Average Reward Metric
ACML 2010
Basis Construction from Power Series Expansions of Value Functions
NeurIPS 2010
Hierarchical Average Reward Reinforcement Learning
JMLR 2007
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs
NeurIPS 2007