Exploration in Deep Reinforcement Learning: A Survey

05/02/2022
by   Pawel Ladosz, et al.
UNIST
OpenAI
0

This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, which means that the agent will not find the reward often by acting randomly. In such a scenario, it is challenging for reinforcement learning to learn rewards and actions association. Thus more sophisticated exploration methods need to be devised. This review provides a comprehensive overview of existing exploration approaches, which are categorized based on the key contributions as follows reward novel states, reward diverse behaviours, goal-based methods, probabilistic methods, imitation-based methods, safe exploration and random-based methods. Then, the unsolved challenges are discussed to provide valuable future research directions. Finally, the approaches of different categories are compared in terms of complexity, computational effort and overall performance.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/24/2023

Successor-Predecessor Intrinsic Exploration

Exploration is essential in reinforcement learning, particularly in envi...
09/14/2021

Exploration in Deep Reinforcement Learning: A Comprehensive Survey

Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Lea...
11/10/2020

Perturbation-based exploration methods in deep reinforcement learning

Recent research on structured exploration placed emphasis on identifying...
02/14/2018

GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms

In continuous action domains, standard deep reinforcement learning algor...
06/29/2021

Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches

This paper surveys the field of multiagent deep reinforcement learning. ...
06/29/2021

DRILL– Deep Reinforcement Learning for Refinement Operators in 𝒜ℒ𝒞

Approaches based on refinement operators have been successfully applied ...

Please sign up or login with your details

Forgot password? Click here to reset