CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning

• URL: http://arxiv.org/abs/2210.01805v1
• Date: Mon, 3 Oct 2022 21:16:14 GMT
• Title: CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning
• Authors: Per-Arne Andersen and Morten Goodwin and Ole-Christoffer Granmo
• Abstract summary: Reinforcement Learning (RL) is a general framework concerned with an agent that seeks to maximize rewards in an environment. There are two approaches, model-based and model-free reinforcement learning, that show concrete results in several disciplines. This paper introduces a novel reinforcement learning algorithm for predicting the distance between two states in a Markov Decision Process.
• Score: 9.432068833600884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement Learning (RL) is a general framework concerned with an agent that seeks to maximize rewards in an environment. The learning typically happens through trial and error using explorative methods, such as epsilon-greedy. There are two approaches, model-based and model-free reinforcement learning, that show concrete results in several disciplines. Model-based RL learns a model of the environment for learning the policy while model-free approaches are fully explorative and exploitative without considering the underlying environment dynamics. Model-free RL works conceptually well in simulated environments, and empirical evidence suggests that trial and error lead to a near-optimal behavior with enough training. On the other hand, model-based RL aims to be sample efficient, and studies show that it requires far less training in the real environment for learning a good policy. A significant challenge with RL is that it relies on a well-defined reward function to work well for complex environments and such a reward function is challenging to define. Goal-Directed RL is an alternative method that learns an intrinsic reward function with emphasis on a few explored trajectories that reveals the path to the goal state. This paper introduces a novel reinforcement learning algorithm for predicting the distance between two states in a Markov Decision Process. The learned distance function works as an intrinsic reward that fuels the agent's learning. Using the distance-metric as a reward, we show that the algorithm performs comparably to model-free RL while having significantly better sample-efficiently in several test environments.

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