Reinforcement Learning
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning Cong Zhang 1, Wen Song
In the paper, we adopt the Proximal Policy Optimization (PPO) algorithm [36] to train our agent. Here we provide details of our algorithm in terms of pseudo code, as shown in Algorithm 1. Similar In this section, we show how the baseline PDRs compute the priority index for the operations. Here we present the complete results on Taillard's benchmark. In Table S.1, we report the results of In Table S.2, we report the generalization performance of our polices trained on The "UB" column is the best solution from The "UB" column is the best solution from Similar conclusion can be drawn from results on DMU benchmark. In Table S.3, we report results In Table S.4 which focuses on The "UB" column is the best solution from The "UB" column is the best solution from We show training curves for all problems in Figure.1.
Adaptive Auxiliary Task Weighting for Reinforcement Learning
Xingyu Lin, Harjatin Baweja, George Kantor, David Held
Neural Information Processing Systems http://nips.cc/
Generalized Off-Policy Actor-Critic
Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson
Neural Information Processing Systems http://nips.cc/
A Unifying View of Optimism in Episodic Reinforcement Learning
In this paper, we provide a new framework for studying this class of algorithms. Optimistic algorithms are built upon the principle of "optimism in the face of uncertainty" (OFU).
Appendix B, we provide sufficient conditions for Assumption 1 that were mentioned in the main
In Appendix A we introduce some basic definitions that are needed for our theoretical results. In Appendix C and Appendix D we prove the error bounds for PPI and PQI. All the other dynamics are preserved. Rewards are 0 for the absorbing action and unchanged elsewhere. Algorithm 1 and 2. As some of the notations is actually a function of the MDP, we clarify the usage Recall the definition of semi-norm of any function of state-action pairs.
Provably Good Batch Reinforcement Learning Without Great Exploration
Batch reinforcement learning (RL) is important to apply RL algorithms to many high stakes tasks.
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
YiDing Jiang, Shixiang (Shane) Gu, Kevin P. Murphy, Chelsea Finn
Solving complex, temporally-extended tasks is a long-standing problem in reinforcement learning (RL).
Conservative Q-Learning for Offline Reinforcement Learning A viral Kumar
Effectively leveraging large, previously collected datasets in reinforcement learning (RL) is a key challenge for large-scale real-world applications. Offline RL algorithms promise to learn effective policies from previously-collected, static datasets without further interaction.
