DDPG and PPO; 2. the effectiveness of reward shaping technique. The combination of deep learning and reinforceme Deep reward shaping from demonstrations - IEEE Conference Publication The goal is to ease the learning for the agent, similar to reward shaping [11]. Robotics. proposed a clipped surrogate objective function that reduces the computation from the constrained optimization. Robotic Bodyguards Using Deep Reinforcement Learning, Direct shape optimization through deep reinforcement learning, Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless With the KL divergence constraint, the policy is guaranteed to improve monotonically. The ICRA-DJI RoboMaster AI Challenge includes a variety of major robotics technologies. The network structure is illustrated in Fig. Deep reinforcement learning algorithms based on experience replay such as DQN and DDPG have demonstrated considerable success in difficult domains such as playing Atari games. 39 G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, Principled reward shaping for reinforcement learning via lyapunov stability theory, Adam: a method for stochastic optimization, T. P. Lillicrap, J. J. ∙ The work of [12] focuses on the reward function design and makes the agent learn from interaction with any opponents and quickly change the strategy according to the opponents in the BattleCity Game which is quite similar with the ICRA-DJI RoboMaster AI Challenge. The main contribution of this paper is divided into two main parts. Hong et al. Introduction. We also use a prioritized replay technique to speed up training speed and the prioritized replay α is 0.6. where the reward is given at winning the match or hitting the enemy, our DRL ∙ Real time strategy games: a reinforcement learning approach. We conclude that a well-set goal can put in question the need for learning optimal". reinforcement and imitation learning by shaping the reward function with a state-and-action-dependent potential that is trained from demonstration data. algorithm rewards our robots when in a geometric-strategic advantage, which Potential-based reward shaping in DQN reinforcement learning. ∙ Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. Model 2 achieves similar performance to Model 1 after 1.2 million episodes and Mode 3’s performance is a little lower than the other two which means sparse reward makes learning more difficult. The first three hidden layers are fully-connected layers with the relu function as the activation function. Although RL shows great promise in sequential decision-making problems in dynamic environments, there are still caveats associated with the framework. A* algorithm with the same implicit geometric goal as DQL and compare results. In this paper we propose a reward shaping method for inte-grating learning from demonstrations with deep reinforcement learning to alleviate the limitations of each technique. Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. On the other hand, Lowe et al. Potential-based reward shaping (PBRS) is a particular category of machine learning methods which aims to improve the learning speed of a reinforcement learning agent by extracting and utilizing extra knowledge while performing a task. It is also given a dense reward. However, learning can be To meet our requirements, we made some modifications. Main Takeaways from What You Need to Know About Deep Reinforcement Learning . The decision-making problem here contains multi-robot cooperation and competition, which requires agents to learn to interact with others in a shared environment. Also, the learning method took hours to train while the A* algorithm only need about 100 milliseconds. [14] discuss several update rules for actor-critic algorithms in multi-agent reinforcement learning that can work well in zero-sum imperfect information games. Reward Shaping I Theorem: R~ admits the same optimal policies as .A. Automating Reward Design. Since all the teams buy the same robots from DJI, we can assume the performance of each robot is the same, which means if one robot and another robot are attacking each other, they have the same health points, cause the same damage, and die at the same time. C. Multi-Objective Reinforcement Learning Multi-objective reinforcement learning [12] (MORL) is an extension to standard reinforcement learning, where the In DDPG, the two target networks are initialized at the start of training, which save the copies of the state-action value function Q(s,a). ∙ University of North Texas ∙ 0 ∙ share . ... algorithms achieve better results. We compare the To do this, they use reward shaping. 1. the enemy robot is generated randomly on the map in every episode. A small step size leads to a slow convergence rate, while a large one tends to affect the sampling from the replay buffer and the estimators of the value function, so the policy improvement is not guaranteed and gives a really poor performance. and competition between robots in a partially observable environment, quite Due to different team strategies, it is also difficult to ensure that the strategy is effective for the opponent and win the game. • Deep-RL algorithms suitable for multi-objective optimization using reward shaping. 4, In PPO, the reward shaping is applied to the estimator of advantage function ^At, which is given in Eq. 0 with/without the improved reward shaping technique, In DDPG, we adopt the reward shaping technique in the actor network based on the TD error, recall TD error, In PPO, the reward shaping is applied to the estimator of advantage function, Intel Core i5-9600K processor, Nvidia RTX 2070 super, 32 GB RAM, Ubuntu 16.04, set up for demonstrating the obstacle avoidance and navigation task in Gazebo is shown in Fig. Bayesian Reward Shaping Ensemble Framework for Deep Reinforcement Learning. Action: A is the action space which contains a set of discrete actions that an agent can take. reinforcement and imitation learning by shaping the reward function with a state-and-action-dependent potential that is trained from demonstration data, using a generative model. Reinforcement learning has demonstrated remarkable performance by maximizing the sum of future rewards through learning policies in Atari games , , , 3D navigation tasks , , robotic arm grabbing objects , , and robot locomotion tasks , .The dense and well-defined reward function can help the agent understand the task and learn skills that might be useful later in life. ∙ The performance with different reward function, Comparison between variant A* algorithm and DQL, G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, J. Bruce, M. Bowling, B. Browning, and M. Veloso, Multi-robot team response to a multi-robot opponent team, 2003 IEEE International Conference on Robotics and Automation (Cat. 4. 0 -table issue by embedding a neural network, however, it still suffers in continuous action tasks. ∙ Value function based methods learn the optimal policy indirectly from value functions. In this paper we propose a novel method for combining learning from demonstrations and experience to expedite and improve deep reinforcement learning. share. And use the last seen position as coordinates if the enemy is not in sight. As a result of the reward engineering principle, the scope of reinforcement learning practice will … 9. performances between the original DDPG and PPO with the revised version of both or more challenging deep reinforcement learning tasks, such as Atari video games [Bellemare et al., 2012] and simulated robotic control [Todorov et al., 2012]. If multi-agent learning is the answer, what is the question? Comparing 3(a) with 4(a) and 3(b) with 4(b) separately, we demonstrate the effectiveness of the reward shaping technique, both DDPG and PPO with reward shaping technique achieve better performances than the original version of them. The following examples highlight this well. Deep Ordinal Reinforcement Learning Alexander Zap, Tobias Joppen, and Johannes Furnkranz TU Darmstadt, 64289 Darmstadt, Germany alexander.zap@stud.tu-darmstadt.de ... numerical feedback signals is the di culty of reward shaping, which is the task of creating a reward function.

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