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Transfer and Multi-task Reinforcement Learning

Introduction

Reinforcement Learning (RL) is a learning paradigm to solve many decision-making problems, which are usually formalized as Markov Decision Processes (MDP). Recently, Deep Reinforcement Learning (DRL) has achieved a lot of success in human-level control problems, such as video games, robot control, autonomous vehicles, smart grids, and so on. However, DRL is still faced with the sample-inefficiency problem especially when the state-action space becomes large, which makes it difficult to learn from scratch. This means the agent has to use a large number of samples to learn a good policy. Furthermore, the sample-inefficiency problem is much more severe in Multiagent Reinforcement Learning (MARL) due to the exponential increase of the state-action space.

This repository contains the released codes of representative benchmarks and algorithms of TJU-RL-Lab on the topic of Transfer and Multi-task Reinforcement Learning, including the single-agent domain and multi-agent domain, addressing the sample-inefficiency problem in different ways.

This repository will be constantly updated to include new research works.

overview

Challenges

Sample-inefficiency problem: The main challenge that transfer and multi-task RL aims to solve is the sample-inefficiency problem. This problem forces the agent to collect a huge amount of training data to learn the optimal policy. For example, the Rainbow DQN requires around 18 million frames of training data to exceed the average level of human players, which is equivalent to 60 hours of games played by human players. However, human players can usually learn an Atari game within a few minutes and can reach the average level of the same player after one hour of training.

Solutions

  • Transfer RL which leverages prior knowledge from previously related tasks to accelerate the learning process of RL, has become one popular research direction to significantly improve sample efficiency of DRL.

    In this repo, we provide specific solutions of our lab including:

    • PTF addresses the Sample-inefficiency problem in DRL by proposing a novel Policy Transfer Framework (PTF). PTF 1) models multiple policy transfer as option learning to learn when and which source policy is the best to reuse for the target policy and when to terminate it; 2) provides an adaptive and heuristic mechanism to ensure the efficient reuse of source policies and avoid negative transfer. Both existing value-based and policy-based DRL approaches can be incorporated and experimental results show PTF significantly boosts the performance of existing DRL approaches, and outperforms state-of-the-art policy transfer methods both in discrete and continuous action spaces.

    • MAPTF addresses the Sample-inefficiency problem in deep MARL by proposing a Multi-Agent Policy Transfer Framework (MAPTF). MAPTF learns which agent's policy is the best to reuse for each agent and when to terminate it by modeling multiagent policy transfer as the option learning problem. Furthermore, to solve the reward conflict problem (each agent's experience may be inconsistent with each other, which may cause the inaccuracy and oscillation of the option-value's estimation) due to the partial observability of the environment, a novel option learning algorithm is proposed, the Successor Representation Option (SRO) learning to solve it by decoupling the environment dynamics from rewards and learning the option-value under each agent's preference. MAPTF can be easily combined with existing deep RL and MARL approaches, and experimental results show it significantly boosts the performance of existing methods in both discrete and continuous state spaces.

    • CAT addresses the Sample-inefficiency problem in cross-domain DRL by proposing a novel framework called Cross-domain Adaptive Transfer (CAT). CAT learns the state-action correspondence from each source task to the target task and adaptively transfers knowledge from multiple source task policies to the target policy. CAT can be easily combined with existing DRL algorithms and experimental results show that CAT significantly accelerates learning and outperforms other cross-domain transfer methods on multiple continuous action control tasks.

  • Multi-task RL, in which one network learns policies for multiple tasks, has emerged as another promising direction with fast inference and good performance.

Directory Structure of this Repo

This repository consists of

  • Single-agent Transfer RL
  • Single-agent Multi-task RL
  • Multi-agent Transfer RL

An overview of research works in this repository:

Category Sub-Categories Method Is Contained Publication Link
Single-agent Transfer RL Same-domain Transfer PTF ✅ IJCAI 2020 https://dl.acm.org/doi/abs/10.5555/3491440.3491868
Single-agent Transfer RL Cross-domain Transfer CAT ✅ UAI 2022 https://openreview.net/forum?id=ShN3hPUsce5
Multi-agent Transfer RL Same task, transfer across agents MAPTF ✅ NeurIPS 2021 https://proceedings.neurips.cc/paper/2021/hash/8d9a6e908ed2b731fb96151d9bb94d49-Abstract.html
Multi-agent Transfer RL Policy reuse across tasks Bayes-ToMoP ✅ IJCAI 2019 https://dl.acm.org/doi/abs/10.5555/3367032.3367121

Liscense

This repo uses MIT Liscense

Acknowledgements

[To add some acknowledgements]

*Update Log

2022-03-18:

  • Repo is created and categories/folders are created.