Learning-Driven Exploration for Reinforcement Learning
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Deep reinforcement learning algorithms have been shown to learn complex skills using only high-dimensional observations and scalar reward. Effective and intelligent exploration still remains an unresolved problem for reinforcement learning. Most contemporary reinforcement learning relies on simple heuristic strategies such as ϵ-greedy exploration or adding Gaussian noise to actions. These heuristics, however, are unable to intelligently distinguish the well explored and the unexplored regions of the state space, which can lead to inefficient use of training time. We introduce entropy-based exploration (EBE) that enables an agent to explore efficiently the unexplored regions of the state space. EBE quantifies the agent's learning in a state using merely state dependent action values and adaptively explores the state space, i.e. more exploration for the unexplored region of the state space. We perform experiments on many environments including a simple linear environment, a simpler version of the breakout game and multiple first-person shooter (FPS) games of VizDoom platform. We demonstrate that EBE enables efficient exploration that ultimately results in faster learning without having to tune hyperparameters.
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