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Found 4 papers, 1 papers with code
Mastering Strategy Card Game (Legends of Code and Magic) via End-to-End Policy and Optimistic Smooth Fictitious Play
Deep Reinforcement Learning combined with Fictitious Play shows impressive results on many benchmark games, most of which are, however, single-stage.
An Entropy Regularization Free Mechanism for Policy-based Reinforcement Learning
We find valued-based reinforcement learning methods with {\epsilon}-greedy mechanism are capable of enjoying three characteristics, Closed-form Diversity, Objective-invariant Exploration and Adaptive Trade-off, which help value-based methods avoid the policy collapse problem.
CASA: Bridging the Gap between Policy Improvement and Policy Evaluation with Conflict Averse Policy Iteration
We study the problem of model-free reinforcement learning, which is often solved following the principle of Generalized Policy Iteration (GPI).
Hierarchical Meta Reinforcement Learning for Multi-Task Environments
To address the problem brought by the environment, we propose a Meta Soft Hierarchical reinforcement learning framework (MeSH), in which each low-level sub-policy focuses on a specific sub-task respectively and high-level policy automatically learns to utilize low-level sub-policies through meta-gradients.
Hierarchical Reinforcement Learning
Meta Reinforcement Learning
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