10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Imitation Learning with Concurrent Actions in 3D Games

      Preprint

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In this work we describe a novel deep reinforcement learning neural network architecture that allows multiple actions to be selected at every time-step. Multi-action policies allows complex behaviors to be learnt that are otherwise hard to achieve when using single action selection techniques. This work describes an algorithm that uses both imitation learning (IL) and temporal difference (TD) reinforcement learning (RL) to provide a 4x improvement in training time and 2.5x improvement in performance over single action selection TD RL. We demonstrate the capabilities of this network using a complex in-house 3D game. Mimicking the behavior of the expert teacher significantly improves world state exploration and allows the agents vision system to be trained more rapidly than TD RL alone. This initial training technique kick-starts TD learning and the agent quickly learns to surpass the capabilities of the expert.

          Related collections

          Most cited references2

          • Record: found
          • Abstract: not found
          • Article: not found

          Measurement of the pressure dependence of air fluorescence emission induced by electrons

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Reinforcement learning in a bio-connectionist model based in the thalamo-cortical neural circuit

              Bookmark

              Author and article information

              Journal
              14 March 2018
              Article
              1803.05402
              5480a218-5bdf-4da4-b180-773470fa33a4

              http://arxiv.org/licenses/nonexclusive-distrib/1.0/

              History
              Custom metadata
              cs.AI cs.LG stat.ML

              Comments

              Comment on this article