Self Imitation Learning

[flrngel]

https://arxiv.org/abs/1806.05635

Abstract

• SIL(Self Imitation Learning) is to verify past good experiences can indirectly drive deep exploration.
• competitive to state-of-the-art

1. Introduction

• Atari "Montezuma's Revenge"
  • ends up with a poor policy (A2C)
  • exploiting the experiences that pick up the key makes able to explore onwards
• Main contributions are,
  1. To study how exploiting past good experiences affects learning (SIL)
  2. Theretical justification of the SIL that is derived from the lower bound of the optimal Q-function
  3. SIL is very simple to implement
  4. Generically applicable to any actor-critic architecture

2. Related work

• Exploration
  • notion of curiousity or uncertainty as a signal for exploration [Schmidhuber, 1991; Strehl & Littman, 2008]
• this paper is different from using that what agent experienced but not yet learned
• Episodic control
  • Lengyel & Dayan, 2008
  • extreme way of exploiting past experiences in the sense that repeats the best outcome in the past
• Experience replay
  • Lin, 1992
    • natural way of exploiting past experiences for parametric policies
  • Prioritized experience replay [Moore & Atkeson; Schaul et al., 2016]
    • prioitizing past experiences based on temporal difference error
  • Optimality tightening He et al., 2017 is similar to this paper
• Experience replay for actor-critic
  • actor-critic framework can also utilize experience replay
  • difference with off-policy and on-policy stackoverflow
  • off-policy evaluation involves importance sampling (ACER, Reactor; use Retrace to evaluate), that may not benefit much from past experience if the policy in the past is very different from current policy
  • this paper does not involve importance sampling and both applicable to discrete and continuous control
• Connection between policy gradient and Q-learning
  • difference with
    Combining policy gradient and Q-learning; PGQL is that paper proposed lower bound Q-learning to exploit good experiences
• Learning from imperfect demonstrations
  • prior works [Liang et al., 2016, Abolafia et al., 2018] used classification loss without justification
  • paper propose a new objective, provide a theoretical justification and systematically investigate how it drives exploration in RL

3. Self Imitation Learning

• goal is to imitate agent's past good experiences in the actor-critic framework
• propose to store past episodes with cumulative rewards in replay buffer
• 
• off-policy actor-critic loss
  
  
  
  

4. Theoretical Justification

5. Experiment

6. Conclusion

• proper level of exploitation of past experiences during learning can drive deep exploration and that SIL and exploration methods can be complementary
• balancing between exploration and exploitation in terms collecting and learning from experiences is an important future research direction