Effects of Reward Terms in AgentBased BoxManipulation Animation Using Deep Reinforcement Learning Agentbased box manipulation has wide applications in computer animation and robotics. Deep reinforcement learning can be applied to generate animations of agentbased box manipulation. This paper focuses on pushmanipulation in an agentbased animation. A policy is learned in a learning session in which an agent receives a reward that is a combination of different types of reward terms. Based on the received reward, the policy is improved gradually. In this paper, we investigate the effects of each reward term indepth in a framework that is integrated with deep reinforcement learning. We also propose a simple way to produce different animation types. We performed several examples and analyzed our findings in details.

The Effect of Different Types of Internal Rewards in Distributed MultiAgent Deep Reinforcement Learning* Distributed multiagent reinforcement learning in the same environment is prohibitively hard, due to the difficulty of assigning credit for the individual actions of the agent, especially when the agent is a member of a team. Meanwhile, the sparse delayed reward about the team from the environment such as winning makes the learning progress more challenging. To solve the credit assignment and sparse delayed reward problems which are common in multiagent reinforcement learning, researchers usually construct or learn an internal reward signal that acts as a proxy for winning and provides denser rewards for individual agent. To improve the learning effect of a typical multiagent learning task, we conducted three types of internal rewards for multiagent team members and evaluated the effect of these rewards. The results show that not all internal reward can improve the learning effect of multiagent reinforcement learning, it seems that when the learning task is not very complex and the time of finishing the task for the team is not very long, the sparse reward such as winning have the best learning effect, and the learning effect of the other two forms of reward is not as good as that of the simple sparse reward. To some extent, our research results can provide a reference for the design of reward function in the application of distributed multiagent reinforcement learning.