Manipulating the Distributions of Experience used for Self-Play Learning in Expert Iteration
Expert Iteration (ExIt) is an effective framework for learning game-playing policies from self-play. ExIt involves training a policy to mimic the search behaviour of a tree search algorithm - such as Monte-Carlo tree search - and using the trained policy to guide it. The policy and the tree search can then iteratively improve each other, through experience gathered in self-play between instances of the guided tree search algorithm. This paper outlines three different approaches for manipulating the distribution of data collected from self-play, and the procedure that samples batches for learning updates from the collected data. Firstly, samples in batches are weighted based on the durations of the episodes in which they were originally experienced. Secondly, Prioritized Experience Replay is applied within the ExIt framework, to prioritise sampling experience from which we expect to obtain valuable training signals. Thirdly, a trained exploratory policy is used to diversify the trajectories experienced in self-play. This paper summarises the effects of these manipulations on training performance evaluated in fourteen different board games. We find major improvements in early training performance in some games, and minor improvements averaged over fourteen games.
Code (1)
Tasks
Board GamesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Computational Account Of Self-Supervised Visual Learning From Egocentric Object Play
Research in child development has shown that embodied experience handling physical objects contributes to many cognitive abilities, including visual learning. One characteristic of such experience is that the learner see…
Contrastive Learningimage-classificationImage ClassificationObjectHindsight Experience Replay
Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay which allows sample-efficient learning from rewards which are s…
Reinforcement LearningReinforcement Learning (RL)ARCHER: Aggressive Rewards to Counter bias in Hindsight Experience Replay
Experience replay is an important technique for addressing sample-inefficiency in deep reinforcement learning (RL), but faces difficulty in learning from binary and sparse rewards due to disproportionately few successful…
continuous-controlContinuous ControlDeep Reinforcement LearningReinforcement Learning+1FedImpro: Measuring and Improving Client Update in Federated Learning
Federated Learning (FL) models often experience client drift caused by heterogeneous data, where the distribution of data differs across clients. To address this issue, advanced research primarily focuses on manipulating…
Federated LearningAttention Loss Adjusted Prioritized Experience Replay
Prioritized Experience Replay (PER) is a technical means of deep reinforcement learning by selecting experience samples with more knowledge quantity to improve the training rate of neural network. However, the non-unifor…
Deep Reinforcement LearningMulti-agent Reinforcement LearningOpenAI Gymreinforcement-learning+1