Intrinsic Exploration as Multi-Objective RL

• URL: http://arxiv.org/abs/2004.02380v1
• Date: Mon, 6 Apr 2020 02:37:29 GMT
• Title: Intrinsic Exploration as Multi-Objective RL
• Authors: Philippe Morere and Fabio Ramos
• Abstract summary: Intrinsic motivation enables reinforcement learning (RL) agents to explore when rewards are very sparse. We propose a framework based on multi-objective RL where both exploration and exploitation are being optimized as separate objectives. This formulation brings the balance between exploration and exploitation at a policy level, resulting in advantages over traditional methods.
• Score: 29.124322674133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intrinsic motivation enables reinforcement learning (RL) agents to explore when rewards are very sparse, where traditional exploration heuristics such as Boltzmann or e-greedy would typically fail. However, intrinsic exploration is generally handled in an ad-hoc manner, where exploration is not treated as a core objective of the learning process; this weak formulation leads to sub-optimal exploration performance. To overcome this problem, we propose a framework based on multi-objective RL where both exploration and exploitation are being optimized as separate objectives. This formulation brings the balance between exploration and exploitation at a policy level, resulting in advantages over traditional methods. This also allows for controlling exploration while learning, at no extra cost. Such strategies achieve a degree of control over agent exploration that was previously unattainable with classic or intrinsic rewards. We demonstrate scalability to continuous state-action spaces by presenting a method (EMU-Q) based on our framework, guiding exploration towards regions of higher value-function uncertainty. EMU-Q is experimentally shown to outperform classic exploration techniques and other intrinsic RL methods on a continuous control benchmark and on a robotic manipulator.

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