Policy search methods directly search for an optimal policy π∗. To evaluate these two algorithms, we count how many times can they create the 2 v.s. The network structure is illustrated in Fig. In contrast to prior RL-based methods that put huge efforts into reward shaping, we adopt the sparse reward scheme, i.e., a UAV will be rewarded if and only if it completes navigation tasks. 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. 2 is the lower bound of Eq. Our algorithm made decisions according to the position of the opponents at each time step but didn’t take the history information into account. Automating Reward Design. So the blue robots are implemented with the variant A* algorithm and the red robots use the trained Deep Q Network from Model 1. We firstly develop the lidar-based enemy detection technique that enhances the robot’s perception capability and turns the POMDP problem into an MDP problem. 8 and Table II, Comparison of performance without reward shaping. Each function, such as self-localization, has its noise because the sensor is not noise-free. One of the central challenges faced by a reinforcement learning (RL) agent is to effectively learn a (near-)optimal policy in environments with large state spaces having sparse and noisy feedback signals. To distinguish these 4 robots concisely, we have the following notation: enemy2: the other one of the enemy robot. Method, Learning control for transmission and navigation with a mobile robot where the reward is given at winning the match or hitting the enemy, our DRL reinforcement and imitation learning by shaping the reward function with a state-and-action-dependent potential that is trained from demonstration data. Reinforcement learning is an approach that helps an agent learn how to make optimal decisions from the environment. the discount factor γ is set to 0.99 to enable the agent a long term view. There are already exist several frameworks solving the first four problems, thus we are focusing on the decision-making problem to bring intelligence to the robots. reinforcement-learning-based AI systems become more general and autonomous, the design of reward mechanisms that elicit desired behaviours becomes both more important and more difficult. These methods have further been extended to multiagent domains in cooperative, competitive or mixed environments. A great challenge in cooperative decentralized multi-agent reinforcement learning (MARL) is generating diversified behaviors for each individual agent when receiv-ing only a team reward. share, In the NIPS 2017 Learning to Run challenge, participants were tasked wit... Value function based methods learn the optimal policy indirectly from value functions. Bayesian Reward Shaping Ensemble Framework for Deep Reinforcement Learning. Real time strategy games: a reinforcement learning approach. 01/28/2019 ∙ by Hassam Ullah Sheikh, et al. For example, in Q-learning, the agent learns from the state-action value function, known as the Q-value, and updates the optimal Q-value in a Q-table as the optimal policy. in Multi-Agent Deep Reinforcement Learning Julien Roy Québec AI institute (Mila) Polytechnique Montréal ... with sparse rewards that require varying levels of coordination as well as on the ... could help discover successful behaviors more efficiently and supersede task-specific reward shaping and curriculum learning. We can see model 1 can reach the highest reward in three models from Fig. ... Introduction. ∙ In our experiment, the enemy robot is treated as a part of the environment and the agent needs millions of trials (2,000,000 episodes) to obtain a good performance. algorithm rewards our robots when in a geometric-strategic advantage, which Since the original A* algorithm will find the shortest path in the grid map and the DQL will also find the optimal path after millions of trials, the performance of these two algorithms actually will become very similar. To simplify the shooting function of the robot, we set up a range, and when the enemy is within that range, we think it’s under attack. ∙ ∙ The reward is given as follow: One step closer, The learning rate of the network is set to 0.01. ∙ propose a dueling network architecture to solve the over-estimate problem in DQL. Model 1 and Model 2 have a similar loss and temporal difference error curves which means these two models can achieve similar results. Due to different team strategies, it is also difficult to ensure that the strategy is effective for the opponent and win the game. Deep Q-learning solves the huge Q. al. algorithms achieve better results. In this multi-agent reinforcement learning problem (MARL)[3][13][15], if each agent treats its experience as part of its (non-stationary) environment which means an agent regards other agents as its environment, the policy it learned during training can fail to sufficiently generalize during execution. Reward Shaping. \Policy invariance under reward transformations: Theory and application to reward shaping". The most intuitive solution to sparse reward problems is reward shaping. ... a RL agent may take an unacceptably long time to discover its goal when learning from delayed rewards, and shaping offers an opportunity to speed up the learning process. Srinivasan et al. This small and fairly self-contained (see prerequisites below) package accompanies an article published in Advances in Neural Information Processing Systems (NeurIPS) entitled "Reinforcement Learning with Multiple Experts: A Bayesian Model Combination … 0 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. According to the training results, we choose Model 1 as our final Model. Also, the computation is reduced due to the KL divergence constraint. The new policy is better than the old one, rt(θ) should be increased so that the better action has a higher probability to be chosen. There is still a huge space for our agents to improve in the future. 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. The ICRA-DJI Robomaster AI Challenge is an important student robot competition as it requires teams to showcase a well-rounded Computer Science skillets. Machine learning practitioners, especially those who deal with reinforcement learning algorithms, encounter a common challenge of making the agent realise that certain task is more lucrative than the other. Deep reinforcement learning utilizes deep neural networks as the function ap-proximator to model the reinforcement learning policy and enables the policy to be trained in an end-to-end manner. Since our robots can communicate with each other, it is easy to fuse the view of two robots, which helps to distinguish which circle is the ally robot, which circle is the enemy robot. each player can individually choose to hunt a stag or hunt a hare. Along with the well-known work on mastering Starcraft II [2] and Dota 2 [3] with reinforcement learning, other Agent1 and agent2 share the same parameters. Reward Shaping in Reinforcement Learning Prasoon Goyal, Scott Niekumand Raymond J. Mooney The University of Texas at Austin fpgoyal, sniekum, mooneyg@cs.utexas.edu Abstract Recent reinforcement learning (RL) approaches have shown strong performance in complex do-mains such as Atari games, but are often highly sample inefcient. 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. Potential-based reward shaping has been shown to be a powerful method to improve the convergence rate of reinforcement learning agents. benefit from excellent Lidar-based Enemy Detection sensing technology that helps us know the enemies’ position, we can consider the Partially Observable Markov Decision Process (POMDP) problem as a Markov Decision Process (MDP). share, In this paper, we study the impact of selection methods in the context o... ∙ However, we found out that a well-defined target can simplify the problem that can be solved even without the need of reinforcement learning. As presented in [2][5], they try to win the game by And use the last seen position as coordinates if the enemy is not in sight. Reinforcement learning and reward shaping are discussed in Sections 3 Reinforcement learning, 4 Reward shaping. This paper proposes the REFUEL1 model, which is a reinforcement learning model with reward shaping and feature rebuilding techniques. Reward shaping is a method of incorporating domain knowledge into reinforcement learning so that the algorithms are guided faster towards more promising solutions. A* algorithm with the same implicit geometric goal as DQL and compare results. Model 1: 2 DQNs share the same parameters. In contrast, for ^At≤0, the action should be disencouraged and rt(θ) should be decreased. ∙ The reward function of reinforcement learning can be designed according to this payoff table to encourage cooperation. We demonstrate that by setting the goal/target of competition in a This paper proposes REFUEL, a reinforcement learning method with two tech-niques: reward shaping and feature rebuilding, to improve the performance of online symptom checking for disease diagnosis. hindered if the goal of the learning, defined by the reward function, is "not The first three hidden layers are fully-connected layers with the relu function as the activation function. With a reward given at each step of agent-environment interaction, the rewards are no longer sparse. Since RL algorithms use rewards as direct feedback to On the other hand, the variant A* algorithm is derived from the same goal but in a more traditional method. Description. Two pos-sible general representations of a task for MDPs are through If two agents are attacking the same enemy which is the stag, two agents obtain the max reward. methods for neuromusculoskeletal environments, Accelerated Robot Learning via Human Brain Signals, Dynamically Feasible Deep Reinforcement Learning Policy for Robot estimating the enemy’s strategy and then adjust their strategy according to the estimated result. After training with r1, the DQL will find the shortest path to achieve the target enemy robot. Many reinforcement learning training algorithms have been developed to date; this article does not cover training algorithms, but it is worth mentioning that some of the most popular ones rely on deep neural network policies. ∙ 3. . communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. The agent can read the type of any grid cell within its sensor FOV(RPLIDAR A3: 360∘ horizontal, 32−cell radial range), which means that it can see any cell that is not obscured by obstacles. In this paper we propose a novel method for combining learning from demonstrations and experience to expedite and improve deep reinforcement learning. A reward shaping technique based on the Lyapunov stability theory proposed in [2] accelerates the convergence of RL algorithms. ^At≥0 means the current action performs better than the others under such a specific state. ∙ The green path in Fig. Reinforcement learning (RL), especially when coupled with deep learning , has gained great success in beyond-human level in Atari games , Go game , cooperative agents , dexterous robotic manipulation and multi-agent RL , among others.However, despite its advanced capabilities, RL suffers severe drawbacks, related to the requirement of enormous training data size, … Active 2 years ago. The frequency of the target network updates every 1000 episode. 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. However, TRPO is difficult to implement and requires more computation to execute. The following examples highlight this well. We believe that this information can help our decision-making module more intelligent. respectively, we could see that PPO converges faster than DDPG and has better averaged reward values. And repeat the whole process several times to eliminate the potential impact of randomness. Main Takeaways from What You Need to Know About Deep Reinforcement Learning . Reinforcement Learning, Negative Update Intervals in Deep Multi-Agent Reinforcement Learning, Proficiency Aware Multi-Agent Actor-Critic for Mixed Aerial and Ground This work combines a spatial autoencoder Reward shaping gives the agent an additional reward to guide the search towards better directions in the sparse feature space. share, We are considering a scenario where a team of bodyguard robots provides 0 3, we demonstrate the neural network chosen for the PPO algorithm in actor-critic style. Reinforcement Learning with Multiple Experts. The simulation results of training are shown on Fig. So that if the enemy is within the range of our robot, which means our robots are attacking the enemy robot, our robots receive a high reward. Reinforcement learning [sutton2018reinforcement] has been successfully applied to various video-game environments to create human-level or even super-human agents [vinyals2019grandmaster, openai2019dota, ctf, vizdoom_competitions, dqn, ye2019mastering], and show promise as a general way to teach computers to play games.However, these results are accomplished with a significant amount … 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). We show that this accelerates policy learning by specifying high-value areas of … 0 Unlike most reward shaping methods, the reward is shaped directly from demonstrations and thus does not need measures that are tailored specifically for a certain task. Lanctot et al. ∙ ∙ communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. ∙ However, giving rewards according to whether the enemy robot is within our robot range leads to very low efficient learning because the number of successes is too small that it is difficult to learn useful things from these successes. 0 Deep reinforcement learning (Deep-RL) algorithms used as solution methods. denoted as A(s). 10/01/2019 ∙ by Iretiayo Akinola, et al. The purpose of reward shaping is to ex-plore how to modify the native reward function without mis-leading the agent. it shows that it creates 2 vs 1 scenarios about four times as many times as DQL. Although RL shows great promise in sequential decision-making problems in dynamic environments, there are still caveats associated with the framework. Recently, with the success of reinforcement learning (RL) in many applications, it has gained more and more attraction in research. It can be difficult to generalize this solution to different opponents. But reward shaping comes with its own set of problems, and this is the second reason crafting a reward function is difficult. The main drawback of DDPG is the difficulty of choosing the appropriate step size. Hong et al. Citation: Hutabarat Y, Ekkachai K, Hayashibe M and Kongprawechnon W (2020) Reinforcement Q-Learning Control With Reward Shaping Function for Swing Phase Control in a Semi-active Prosthetic Knee. As we can see the green path from Fig. The experimental results are listed statistically in Table. The green path can also be obtained by the original A* algorithm in this situation, which does not meet our requirement. (DQL) to generate multi-agent paths for moving, which improves the cooperation So a punishment item is added to r1 to encourage avoiding another enemy robot while moving. We total training episode number is 2,000,000 and the replay buffer’s size is 1,000,000. we test three kinds of models to see which perform better. However, learning can be hindered if the goal of the learning, defined by the reward function, is "not optimal". ∙ 06/01/2018 ∙ by Yang Lyu, et al. In this paper, we consider solving the obstacle avoidance and navigation problem for unmanned ground vehicles by applying DDPG and PPO equipped with a reward shaping technique. We test two kinds of networks and evaluate their performance from mean episode reward and loss. The clipped surrogate objective function is given by: Compared to TRPO, the probability ratio rt(θ) is clipped between [1−ϵ,1+ϵ], in practice, we choose ϵ=0.2, which means no matter how good the new policy, the rt(θ) only increases 20% at most. The action dimension is 2, one is changing the linear velocity, while the other is changing the angular velocity. 8 shows the path generated under such a reward function. For the future directions, we will investigate the performance of PPO applied to multi-agent robots systems and combine the SLAM techniques and reinforcement learning to improve the performance. Recent developments in the field of deep reinforcement learning (DRL) have shown that reinforcement learning (RL) techniques are able to solve highly complex problems by learning an optimal policy for autonomous control tasks. ∙ or more challenging deep reinforcement learning tasks, such as Atari video games [Bellemare et al., 2012] and simulated robotic control [Todorov et al., 2012]. 12/01/2017 ∙ by Yiding Yu, et al. share, We present a novel Deep Reinforcement Learning (DRL) based policy for mo... combine reinforcement learning with a deep neural network, the experience replay and fixed Q-targets mechanism, which achieves human-level control on Atari games. One agent has 5 actions, so this network’s action space is grown to 25. Ask Question Asked 2 years ago. 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 exploration fraction parameter is set to 0.8 initially and it will be linearly decreased gradually during the learning process until 0.3. In our case, we can regard our two robots are the players and the enemies are the stag and the hare. We conclude that a well-set goal can put in question the need for learning 1999 I Alternative proof: advantage function is invariant. By applying the actor-critic framework while learning a deterministic policy, DDPG is able to solve continuous space learning tasks. 1. Comparing 3(a) with 3(b) and 4(a) with 4(b) respectively, we could see that PPO converges faster than DDPG and has better averaged reward values. Comparison of performance with reward shaping. arXiv preprint arXiv:1606.01541, 2016. 0 ∙ in the robotic control area. ∙ Bayesian Reward Shaping Ensemble Framework for Deep Reinforcement Learning. DDPG is a breakthrough that enables agents to choose actions in a continuous space and perform well. magnitude. In this paper, we investigate the obstacle avoidance and navigation problem in the robotic control area. • Deep-RL algorithms outperform feedback control heuristic on different objectives. This small and fairly self-contained (see prerequisites below) package accompanies an article published in Advances in Neural Information Processing Systems (NeurIPS) entitled "Reinforcement Learning with Multiple Experts: A Bayesian Model Combination … 3.2 Reward Shaping Reward shaping is a useful method to incorporate auxiliary knowledge safely. A ros package for 2d obstacle detection based on laser range data. 12 stag: the enemy robot we want to attack, selected from enemy1 and enemy2, hare: the enemy robot we want to avoid and not attack, selected from enemy1 and enemy2. -table issue by embedding a neural network, however, it still suffers in continuous action tasks. Since the position of the obstacles(the wall in this competition) will not change, to simplify the observation, we set observation as the positions of the four robots(agent, ally, enemy1,enemy2). Feature rebuilding can guide the agent to learn correlations between features. We compare DDPG and PPO in the same learning settings and the simulations show that PPO has a better performance than DDPG, and the proposed algorithms help RL achieve better results. State: S is the state space which can be discrete or continuous. Transition: T(s,a) is the state transition function s′=T(s,a), gives the environment’s transition probability to state. In Section 5 our method to learn a potential function for reward shaping in model-free RL is introduced, and in Section 6 a corresponding algorithm for the model-based case is presented. 4, In PPO, the reward shaping is applied to the estimator of advantage function ^At, which is given in Eq. In this paper, we mainly focus on UGVs. Deep Q-Networks, which adapt deep reinforcement learning to ordinal rewards. ∙ The ICRA-DJI RoboMaster AI Challenge is a game of cooperation algorithms, with geometric-based searches outperforming DQL in many orders of 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. 3. where η is a tuning parameter that weights the shaped term γR(st+1,at+1)−R(s,a). Introduction. 5. Each grid cell is assigned a static class (wall, empty, robot). This action space shaping comes in the forms of removing actions, combining different actions into one action and dis-cretizing continuous actions. Active 2 years ago. Since our input is not a figure, there is no need to use the convolution layer to extract features. proposed a clipped surrogate objective function that reduces the computation from the constrained optimization. The action space consists of five discrete actions: Up, Down, Left, Right and Stop. We divide the comparison of performances into two parts: 1. Environment Shaping in Reinforcement Learning using State Abstraction. between two robots by avoiding the collision. similar to the Counter-Strike game. while our robot is moving toward the stag, we don’t want to be attacked by another robot, so it requires us to plan the path to avoid another robot. If the center of a circle is inside a wall, we filter out this circle. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. Using UGVs and UAVs for military-based scenarios has multiple benefits including reducing the risk of death by replacing human operators. DDPG and PPO; 2. the effectiveness of reward shaping technique. Gazebo, ROS, and Turtlebot 3 Burger® are used as a platform to demonstrate the proposed algorithms and compare the performances with/without the improved reward shaping technique when applied to the same real mobile robotic control problem. Robotic Bodyguards Using Deep Reinforcement Learning, Direct shape optimization through deep reinforcement learning, Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless 2.3 Inverse Reinforcement Learning IRL is the problem of learning a reward function using a set of observations from expert demonstrations [15, 1]. ∙ Prior studies have paid many efforts on reward shaping or designing a centralized critic that can discriminatively credit the … On the other hand, Lowe et al. The result is shown in Fig. Reward Shaping in Reinforcement Learning Prasoon Goyal, Scott Niekum, Raymond J. Mooney Department of Computer Science The University of Texas at Austin fpgoyal, sniekum, mooneyg@cs.utexas.edu Abstract Recent reinforcement learning (RL) approaches have shown strong performance in complex do-mains such as Atari games, but are often highly The original A* algorithm can find the shortest path from agent1 to stag in the grid map. implicitly increases the winning chances. ∙ Reinforcement learning is the most promising candidate for truly scalable, human-compatible, AI systems, and for the ultimate progress towards Artificial General Intelligence (AGI). We use Gazebo, ROS, and Turtlebot 3 Burger® to demonstrate both DDPG and PPO separately. And the reward will increase if 2 robots go to attack the same enemy at the same time to encourage cooperation according to the stag hunt strategy. share. In general, , reinforcement learning is modeled as a Markov Decision Process (MDP), With the growth of complex environments, such as UGV applications, , combining deep learning and reinforcement in continuous action space control learning has attracted. Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs) are widely used in both civil and military applications. The network receives the state vector which is an 8 dimensions vector as the input. The drawbacks of Q-learning are obvious, for example, a Q-table will explode when handling complex tasks. Training details are given in Table 1. Depending on this, we conclude that a well-set, implicit goal can simplify a problem and allow us to use a relatively low-level algorithm to solve a problem that could have required hours of computational time with a learning algorithm. However, learning can be In this paper, we investigate the obstacle avoidance and navigation problem share. 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. Model 2: Since agent1 and agent2 have the same goal and there is no conflict between them, that means we can use 1 DQN to controls two agents at the same time. Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging simulated continuous control single agent tasks. So we modified the reward that it is given corresponding to the distance between agent1 and stag.
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