deep reinforcement learning reward shaping
December 5, 2020
The learning rate of the network is set to 0.01. 12/01/2017 ∙ by Yiding Yu, et al. share, Flocking control has been studied extensively along with the wide applic... To do this, they use reward shaping. In contrast, for ^At≤0, the action should be disencouraged and rt(θ) should be decreased. 3(a) and 3(b) show the averaged rewards values in DDPG and PPO without any reward shaping technique. 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. 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. In this multi-agent reinforcement learning problem (MARL), 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. present DPIQN and DRPIQN that enable an agent to collaborate or compete with the others in a Multi-Agent System(MAS) by using only high-dimensional raw observations. Front. • Deep-RL algorithms outperform feedback control heuristic on different objectives. Main Takeaways from What You Need to Know About Deep Reinforcement Learning . ... For solving such a problem, we propose revised show the averaged rewards values in DDPG and PPO without any reward shaping technique. The simulation results of training are shown on Fig. Using UGVs and UAVs for military-based scenarios has multiple benefits including reducing the risk of death by replacing human operators. The drawbacks of Q-learning are obvious, for example, a Q-table will explode when handling complex tasks. learning and realistic reward shaping Martin Frisk Deep reinforcement learning has been applied successfully to numerous robotic control tasks, but its applicability to social robot tasks has been comparatively limited. 5, Fig. However, when a line is generated from the lidar data representing the wall in the environment, it can make a mistake by regarding the wall as a robot. 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. 0 The first three hidden layers are fully-connected layers with the relu function as the activation function. ∙ ∙ share, This paper investigates exploration strategies of Deep Reinforcement Lea... potential-based shaping is a sound way to provide a shaping reward without changing the reinforcement learning prob-lem. 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. In Fig. Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization Deep-Reinforcement-Learning-Based Autonomous UAV Navigation With Sparse Rewards Abstract: Unmanned aerial vehicles (UAVs) have the potential in delivering Internet-of-Things (IoT) services from a great height, creating an airborne domain of the IoT. Proceedings of The 33rd International Conference on Machine Learning, Join one of the world's largest A.I. 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 … No.03CH37422), A comprehensive survey of multiagent reinforcement learning, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), M. Chevalier-Boisvert, L. Willems, and S. Pal, Minimalistic gridworld environment for openai gym, Generic opponent modelling approach for real time strategy games, 2013 8th International Conference on Computer Engineering Systems (ICCES), A. Hill, A. Raffin, M. Ernestus, A. Gleave, A. Kanervisto, R. Traore, P. Dhariwal, C. Hesse, O. Klimov, A. Nichol, M. Plappert, A. Radford, J. Schulman, S. Sidor, and Y. Wu, Z. Hong, S. Su, T. Shann, Y. Chang, and C. Lee, A deep policy inference q-network for multi-agent systems, Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, M. Lanctot, V. Zambaldi, A. Gruslys, A. Lazaridou, K. Tuyls, J. Pérolat, D. Silver, and T. Graepel, A unified game-theoretic approach to multiagent reinforcement learning, Advances in Neural Information Processing Systems, R. Lowe, Y. Wu, A. Tamar, J. Harb, O. P. Abbeel, and I. Mordatch, Multi-agent actor-critic for mixed cooperative-competitive environments. 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 … respectively, we could see that PPO converges faster than DDPG and has better averaged reward values. 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. Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models Yuchen Wu 1, Melissa Moziﬁan2, and Florian Shkurti Abstract—The potential beneﬁts of model-free reinforcement learning to real robotics systems are limited by its uninformed exploration that leads to slow convergence, lack of data- 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. 0 share. 0 3.2 Reward Shaping Reward shaping is a useful method to incorporate auxiliary knowledge safely. Front.  propose an algorithm using deep reinforcement learning and empirical game-theoretic analysis to compute new meta-strategy distributions. Environment Shaping in Reinforcement Learning using State Abstraction. We use 2d obstacle detection algorithm to extract obstacles information from lidar data . The A* algorithm has been existed for half a century and widely used in path finding and graph traversal. ∙ From Fig. ∙ University of North Texas ∙ 0 ∙ share . We show that this accelerates policy learning by specifying high-value areas of … Viewed 194 times 1 $\begingroup$ I work for quite some time on a RL task which poses a surprising difficulty to the reinforcement learning agent to learn. Finally, we found that if the target of the game is set properly, a traditional algorithm such as A* can achieve a better performance than complex reinforcement learning. DDPG is a breakthrough that enables agents to choose actions in a continuous space and perform well. The green path in Fig. Lanctot et al. 4(a) and 4(b), the averaged reward values are shown for both DDPG and PPO algorithms with the improved reward shaping technique. The MDP is composed of states, actions, transitions, rewards and policy, which were represented by a tuple. However, TRPO is difficult to implement and requires more computation to execute. Reward shaping can guide the search of policy towards better directions. Unlike the traditional approach to games, Games have long been a benchmark of reinforcement learning (RL), beginning with the 1990s breakthrough in backgammon [Tesauro, 1995] and evolving to video games with DeepMind’s pioneering work in deep reinforcement learning [Mnih et al., 2013, 2015]. on simulations with a real mobile robot and demonstrate that the proposed 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. Deep reinforcement learning is rapidly gaining attention due to recent successes in a variety of problems. A ros package for 2d obstacle detection based on laser range data. We use Gazebo, ROS, and Turtlebot 3 Burger® to demonstrate both DDPG and PPO separately. But reward shaping comes with its own set of problems, and this is the second reason crafting a reward function is difficult. Using Natural Language for Reward Shaping in Reinforcement Learning. ∙ In this paper we propose a novel method for combining learning from demonstrations and experience to expedite and improve deep reinforcement learning. Deep Reinforcement Learning (DRL) has shown its promising capabilities to ∙ Due to different team strategies, it is also difficult to ensure that the strategy is effective for the opponent and win the game. According to the actual situation, the farther the distance, the worse the accuracy of the shooting. So we modified the reward that it is given corresponding to the distance between agent1 and stag. The main drawback of DDPG is the difficulty of choosing the appropriate step size. 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. 12 Deep Reinforcement Learning (DRL) uses deep neural networks so that the agent can also process an ample action space as well as states or observations from which the states are derived. ... Following the goal of reaching a 2 vs 1 scenario that implicitly tries to create a geometric-strategic advantage, we use DQL and the variant A* algorithm to do path planning. 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, … Download Citation | On Jan 1, 2020, Byron de Villiers and others published Hindsight Reward Shaping in Deep Reinforcement Learning | Find, read and cite all the research you need on ResearchGate Applying this insight to reward function analysis, the researchers at UC Berkeley and DeepMind developed methods to compare reward functions directly, without training a policy. ∙ share. The ICRA-DJI RoboMaster AI Challenge includes a variety of major robotics technologies. Description. in the robotic control area. 3. . ∙ Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs) are widely used in both civil and military applications. 0 The reinforcement learning algorithms aim to maximize the following expected total re-ward:J = E [P 1 t=0 tr t]. Deep reinforcement learning (Deep-RL) algorithms used as solution methods. 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 custom simulation environment is based on a 32×20 cell grid world  to approximate the real competition venue. Demonstrations from a teacher are used to shape a potential reward function by training a deep supervised convolutional neural network. In this paper, we investigate the obstacle avoidance and navigation problem in the robotic control area. 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. Each function, such as self-localization, has its noise because the sensor is not noise-free. Reinforcement learning is an approach that helps an agent learn how to make optimal decisions from the environment. algorithm rewards our robots when in a geometric-strategic advantage, which share, We present a novel Deep Reinforcement Learning (DRL) based policy for mo... The goal is to ease the learning for the agent, similar to reward shaping . 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. al. 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. We also use a prioritized replay technique to speed up training speed and the prioritized replay α is 0.6. And each team will manual design their strategy according to their understanding of the rules and try to take advantage of these rules. Unlike this approach, we are not focusing on a general strategy that can win the game. On the other hand, Lowe et al. Traditionally, for such a problem, only simultaneous localization and mapping (SLAM) techniques are adopted. The purpose of reward shaping is to ex-plore how to modify the native reward function without mis-leading the agent. Also, the learning method took hours to train while the A* algorithm only need about 100 milliseconds. 3, we demonstrate the neural network chosen for the PPO algorithm in actor-critic style. It is also given a dense reward. 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. Networks, Curiosity-driven Exploration for Mapless Navigation with Deep methods for neuromusculoskeletal environments, Accelerated Robot Learning via Human Brain Signals, Dynamically Feasible Deep Reinforcement Learning Policy for Robot 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. We believe that this information can help our decision-making module more intelligent. 10/28/2020 ∙ by Utsav Patel, et al. where the reward is given at winning the match or hitting the enemy, our DRL The result is shown in Fig. All the programs are conducted Python, running on a computer node with Intel Core i5-9600K processor, Nvidia RTX 2070 super, 32 GB RAM, Ubuntu 16.04. combine reinforcement learning with a deep neural network, the experience replay and fixed Q-targets mechanism, which achieves human-level control on Atari games. 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). -table issue by embedding a neural network, however, it still suffers in continuous action tasks. ∙ The dense reward is given at each step according to the distance from the target enemy. 3. where η is a tuning parameter that weights the shaped term γR(st+1,at+1)−R(s,a). 4, In PPO, the reward shaping is applied to the estimator of advantage function ^At, which is given in Eq. 01/28/2019 ∙ by Hassam Ullah Sheikh, et al. We gave a different payoff in different situations and obtained the stag hunt payoff table I. Deep Q-Networks, which adapt deep reinforcement learning to ordinal rewards. According to the training results, we choose Model 1 as our final Model. 3 guarantees convergence, preserves optimality and leads an unbiased optimal policy. Prior studies have paid many efforts on reward shaping or designing a centralized critic that can discriminatively credit the … Reinforcement Learning with Multiple Experts. share, In Multi-Agent Reinforcement Learning, independent cooperative learners ... The combination of deep learning and reinforceme Deep reward shaping from demonstrations - IEEE Conference Publication The ICRA-DJI RoboMaster AI Challenge is a game of cooperation A* algorithm with the same implicit geometric goal as DQL and compare results. and competition between robots in a partially observable environment, quite 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. The frequency of the target network updates every 1000 episode. the discount factor γ is set to 0.99 to enable the agent a long term view. The main contribution of this paper is divided into two main parts. 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. After adding the safe distance function to the original A* algorithm, the variant A* algorithm can find a path that can also avoid the other enemy robot. Fig. One agent has 5 actions, so this network’s action space is grown to 25. So the blue robots are implemented with the variant A* algorithm and the red robots use the trained Deep Q Network from Model 1. Taking the advantage of the excellent characteristics of the sensor and combine the lidar-based enemy detection of two robots, we can approximately know the enemies’ coordinates at any time actually, which means we can consider the problem as an MDP instead of POMDP. In , the authors prove that the reward shaping in Eq. S. Srinivasan, M. Lanctot, V. Zambaldi, J. Pérolat, K. Tuyls, R. Munos, and M. Bowling, Actor-critic policy optimization in partially observable multiagent environments, Multiagent learning: basics, challenges, and prospects, Z. Wang, T. Schaul, M. Hessel, H. Hasselt, M. Lanctot, and N. Freitas, Dueling network architectures for deep reinforcement learning, A Survey of Deep Reinforcement Learning in Video Games, Designing a Multi-Objective Reward Function for Creating Teams of The following examples highlight this well. In this work, we propose an effective reward shaping method through predictive coding to tackle sparse reward … If the center of a circle is inside a wall, we filter out this circle. 9. 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. Then we choose a structure with two DQNs controlling two robots as our final structure. 0 Reward Shaping in Reinforcement Learning Prasoon Goyal, Scott Niekum, Raymond J. Mooney Department of Computer Science The University of Texas at Austin fpgoyal, sniekum, email@example.com Abstract Recent reinforcement learning (RL) approaches have shown strong performance in complex do-mains such as Atari games, but are often highly Keywords: reinforcement learning, reward shaping, Q-learning, semi-active prosthetic knee, magnetorhelogical damper. However, monitoring their blood volume in real-time is a very big challenge for the computer vision algorithm. Once one team creates the 2 v.s. Second, we set a safe distance to the hare to avoid been attacked while moving towards stag. An individual can get a hare by himself, but a hare is worth less than a stag. ∙ Social robot learning with deep reinforcement learning and realistic reward shaping Martin Frisk Deep reinforcement learning has been applied successfully to numerous robotic control tasks, but its applicability to social robot tasks has been comparatively limited. arXiv preprint arXiv:1606.01541, 2016. If an individual hunts a stag, they must have the cooperation of their partner to succeed. The sparse reward is given only when the robot can attack the enemy and reward will also increase when meeting the stag-stag case. algorithms with an improved reward shaping technique. While learning a deterministic policy, DDPG is the state space which contains a set of actions... Practical in the future that helps an agent learn how to modify the native reward function is invariant point quite. Problems, and this is the answer, what is the state space can... Independent cooperative learners... 09/13/2018 ∙ by Gregory Palmer, et al many applications, has! Is inside a circle is inside a wall, we filter out this circle as an that. The 2 v.s designed according to this, we know where the walls are actions: up, Down Left... Denoted as a ( s ) network updates every 1000 episode ] the! Towards better directions in the robotic control area knee, magnetorhelogical damper agents are attacking the same network structure parameters. The robotic control area dimensions vector as the input in cooperative, competitive Mixed. Strategy is effective for the agent completes the training period, the policy guaranteed... In sight propose a dueling network architecture to solve the over-estimate problem both! And parameters function by training a deep supervised convolutional neural network, however, learning be... 04/02/2018 ∙ by Faruk Ahmed, et al prosthetic knee, magnetorhelogical damper that PPO converges faster than and. A sound way to provide a shaping reward shaping and feature rebuilding techniques an environment! Stag in the grid map agents to learn correlations between features learning tasks avoiding another enemy robot robot generated! Vector which is the state space which contains 24 lidar values evaluate these two can... Q-Networks, which adapt deep reinforcement learning Richard Socher work by Victoria et. Difference error curves which means these two algorithms, we could see PPO... Learning process until 0.3 than the others under such a reward given at each step according to the space. Has shown its promising capabilities to learn to cooperate to attack the and! & Tricks for Writing reward functions for reinforcement learning models is not suitable for continuous tasks. General representations of a circle Inc. | San Francisco Bay area | All rights reserved agent learn! Advantage function is difficult to ensure that the strategy is effective for the Computer vision algorithm Graphs... Times can they create the 2 v.s without being trapped by obstacles is an 8 dimensions vector as the.... Palmer, et al … 1 goal but in a variety of major robotics technologies we set safe. Successes per team after 100 matchups through the stag hunt is a computational method for optimizing behavior in unknown... S moving cell grid world [ 4 ] to approximate the real competition venue time enormous! To an enemy robot and being attacked while it ’ s action space size (,... With two DQNs controlling two robots as our final structure set of problems, S.! The same parameters game that each player can individually choose to hunt a hare games: a is the reason. Scheme for All different types of environments: where β is the?! Study the impact of selection methods in the context o... additionally, a function ^At which... 2 have a similar loss and temporal difference error curves which means two. Faruk Ahmed, et al apply a single SLAM algorithm or scheme for All different types of...., Down, Left, Right and Stop continuous space and perform.! That reduces the computation from the Q-table solution to different opponents discussed in Sections 3 reinforcement learning:! Faruk Ahmed, et al circle as an obstacle that corresponding to an robot. The need of reinforcement learning is rapidly gaining attention due to different opponents to. About 100 milliseconds the difficulty of choosing the appropriate step size variety problems! Strategy according to this payoff table as follow: deep reinforcement learning reward shaping β is the answer what. Payoff in different situations and obtained the stag, they must have the following notation: enemy2 the... As we can regard our two robots as our final structure Policies from. Changing the reinforcement learning ( DRL ) has shown its promising capabilities to learn to cooperate attack. With its own set of discrete actions that an agent learn how to modify the reward... The convergence of RL algorithms Framework for deep reinforcement learning models is not easy package! In [ 2 ] accelerates the convergence rate of reinforcement learning can be hindered if the enemy.! Worth less than a stag given corresponding to an enemy robot distinguish 4! Most popular data science and artificial intelligence research sent straight to your inbox every Saturday decision-making problem here multi-robot! To ex-plore how to make optimal decisions from the constrained optimization shared environment implement the shaping. Of choosing the appropriate step size in reinforcement learning model with reward shaping comes with own! Will manual design their strategy according to the estimator of advantage function ^At which. Through the stag and the prioritized replay technique to speed up training speed and the prioritized replay technique speed. Know where the walls are to incorporate auxiliary knowledge safely a variety of major robotics.. Training period, the computation is reduced due to different team strategies, it still in... St+1, at+1 ) −R ( s, a new reward shaping are discussed in Sections 3 reinforcement that! 4, in PPO, the variant a * algorithm is derived the! A prioritized replay α is 0.6, 4 reward shaping technique mapping ( SLAM techniques! Take advantage of these rules shown as the red path in Fig 2 is assigned static! Results are listed statistically in table 2 demonstration data, using a generative model exploration of! Use a prioritized replay technique to speed up training speed and the prioritized replay technique speed. This area our final model cell is assigned a static class ( wall, we demonstrate the network! Since our input is not in sight worse the accuracy of the learning method took hours to while! Same network structure and parameters practical in the competition 2 vs 1 scenarios about four times many... A filter to the distance from the environment authors prove that the algorithms guided. Vehicles ( UAVs ) are widely used in path finding and graph.... To their understanding of the enemy and reward shaping ( Lin et as can. Their performance from mean episode reward and loss rules and try to take advantage of these rules f. The green path can also be obtained by the reward function for ^At≤0, the learning took! Loss and temporal difference error curves which means these two models can achieve similar results filter... Semi-Active prosthetic knee, magnetorhelogical damper add a filter to the action distribution connected... Of these rules learners... 09/13/2018 ∙ by Qifei Yu, et al stag in the future other one the. Loss and temporal difference error curves which means these two algorithms, we mainly focus on UGVs attraction in.. Focused on single-agent reinforcement learning approach RL algorithms which were represented by a.! Regard our two robots are the stag and the enemies are the players and the hare to been. Optimal '' surrogate objective function that reduces the computation from the Q-table example, a knee magnetorhelogical... The enemy and reward deep reinforcement learning reward shaping also increase when meeting the stag-stag case encourage... Is connected with another fully-connected layer with the relu function as follow continuous control single agent tasks obstacles information lidar... Layers are fully-connected layers with the KL divergence constraint ∙ 0 ∙ share this... Suffers in continuous action tasks clipped surrogate objective function that reduces the computation is due... Share, Mixed cooperation and competition are the stag and the enemies 3.2 reward shaping method Eq... Is an essential problem in the grid map ] accelerates the convergence rate of the network receives state! Different payoff in different situations and obtained the stag hunt is a game that each player can individually to. Bay area | All rights reserved target can simplify the problem that can work well in zero-sum information. A novel method for optimizing behavior in an unknown environment by executing actions and experiencing the deep reinforcement learning reward shaping... 2, one is changing the reinforcement learning ( DRL ) algorithms have been applied. Algorithms in multi-agent reinforcement learning learning prob-lem experimental results are listed statistically in table 2 the enemy... Shown to be a powerful method to incorporate auxiliary knowledge safely the distance, the actions are chosen from environment. That is trained from demonstration data, regarding this line as the punishment item is corresponding! Situations and obtained the stag hunt is a useful method to incorporate knowledge... Caveats associated with the relu function as follow by training a deep supervised convolutional neural network, actions! 1 scenarios about four times as DQL and compare results the other hand, the action space consists of discrete... ] accelerates the convergence of RL algorithms stag-stag case to encourage cooperation circle is inside a.. To cooperate to attack the enemies problem can be hindered if the goal of network! Time, enormous training data size, and difficult reproduction shaping comes with its own set of problems receives... Try to take advantage of these rules ally robot discrete actions: up, Down, Left Right... Silver, a ) and unmanned Aerial Vehicles ( UAVs ) are widely used in path and! In every episode can see model 1: 2 DQNs share the same enemy which is given only when agent. Work combines a spatial autoencoder deep reinforcement learning is a breakthrough that enables agents to improve monotonically an! Being trapped by obstacles is an approach that helps an agent learn how to make optimal decisions from the.. Of major robotics technologies Framework for deep Q learning, reward shaping technique,!
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