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Guided Deep Reinforcement Learning for Swarm Systems
Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann
TL;DR
Cooperative swarm control is difficult when agents have limited sensing and only local observations, while manual rule design can limit task complexity. The paper trains an actor-critic framework with a globally informed critic and locally acting agents. It reports learned policies for distance maintenance and target location, while concluding that the non-guided approach was not feasible.
Problem
Cooperative policies are difficult to learn when agents have limited local observations, and manually defining behaviors can limit task complexity.
Source: §1 · p. 2Method
A shared policy selects agents' actions from local histories while a guided critic learns with global state information.
Source: §3.1 · p. 5, §3.2 · p. 5Results
The authors learned policies for maintaining distances and cooperatively locating a target, and report that the non-guided approach was not feasible.
Source: §1 · p. 2, §5 · p. 14Takeaways & Limitations
The authors argue that global-state guidance has potential to extend deep reinforcement learning to cooperative multi-agent scenarios such as robot swarms.
Source: §5 · p. 14In the non-guided setting, the authors cite sharply increased problem dimensionality and a history of length 10 that contains too little information about the global state.
Source: §4.6 · p. 13
Abstract
from arXiv · showhide
In this paper, we investigate how to learn to control a group of cooperative agents with limited sensing capabilities such as robot swarms. The agents have only very basic sensor capabilities, yet in a group they can accomplish sophisticated tasks, such as distributed assembly or search and rescue tasks. Learning a policy for a group of agents is difficult due to distributed partial observability of the state. Here, we follow a guided approach where a critic has central access to the global state during learning, which simplifies the policy evaluation problem from a reinforcement learning point of view. For example, we can get the positions of all robots of the swarm using a camera image of a scene. This camera image is only available to the critic and not to the control policies of the robots. We follow an actor-critic approach, where the actors base their decisions only on locally sensed information. In contrast, the critic is learned based on the true global state. Our algorithm uses deep reinforcement learning to approximate both the Q-function and the policy. The performance of the algorithm is evaluated on two tasks with simple simulated 2D agents: 1) finding and maintaining a certain distance to each others and 2) locating a target.
1 Introduction
Swarm robotics seeks collective behavior from simple agents with limited local sensing, but manually extracting rules is tedious and limits task complexity. This paper proposes deep reinforcement learning for cooperative agents, with centralized global-state guidance during training and locally based decisions; it demonstrates the approach on distance maintenance and target location.
1 Introduction
- Collective behavior can exceed individual capabilities, while swarm robots typically have limited local sensing and communication.Such constraints motivate emulating collective behavior with simple robotic entities.Source: §1 · p. 1
- Manually defining cooperative behaviors can be tedious and limits the complexity of tasks solved manually.Prior work inferred rules from observed behavior, including rules for cooperative box-pushing based on ant prey retrieval.Source: §1 · p. 2
- The proposed actor-critic approach trains decentralized policies on local observations while a critic evaluates actions using the full system state.The global state may be available centrally during training, for example from a camera filming the swarm.Source: §1 · p. 2
- The deep reinforcement learning framework learns policies for homogeneous cooperative agents, using global state for Q-function learning and local observations for actions.The paper notes that existing deep-learning extensions for partial observability were formulated only for single-agent scenarios [1], [3].Source: §1 · p. 2
- In simulated Kilobot-inspired tasks, agents learn to maintain distances from one another and cooperatively locate a target using sensed distances without direction.Agents can move forward or turn and sense neighboring agents within a certain radius.Source: §1 · p. 2
2 Background
The background introduces DQN and DDPG as single-agent reinforcement learning foundations. DQN approximates a Q-function for continuous states and discrete actions; DDPG extends the actor-critic approach to deterministic continuous control.
2 Background
- The background identifies DQN and DDPG as two single-agent reinforcement learning algorithms reviewed for the paper.Source: §2 · p. 2
- Figure 1 shows the Kilobot platform used as the basis for the paper's simulated agents.Source: Figure 1 · p. 3
2.1 The Deep Q-Learning Algorithm
- DQN approximates a Q-function with a deep network for continuous state and discrete action spaces.Target networks and experience replay stabilize updates; transitions are sampled in mini-batches, and the final policy is greedy.Source: §2.1 · pp. 3–4
2.2 The Deep Deterministic Policy Gradient Algorithm
- DDPG extends DQN to deterministic continuous control through simultaneous actor and critic networks.It uses slowly tracking target networks and experience replay; the actor update follows the chain rule, with inverted gradients bounding actions [2].Source: §2.2 · p. 4
3 Deep Reinforcement Learning for Guided Swarm Learning
The framework trains a shared policy for homogeneous agents using local observation histories, while a centralized critic evaluates joint actions from global state. It represents variable neighbor-distance sensing with fixed-dimensional histograms.
3 Deep Reinforcement Learning for Guided Swarm Learning
- Global state guides centralized evaluation during training, while each homogeneous agent chooses actions from its own observation history.In the simulated experiments, the central state comprises all robots’ positions and orientations and is not shared with agents.Source: §3 · p. 4
3.1 Problem Statement
- The swarm MDP models identical agents with a shared local-history policy and rewards based on global state and all agents’ actions.The formulation is a special case of a decentralized partially observed Markov decision process.Source: §3.1 · p. 5
3.2 Centralized Guided Critic
- The centralized critic evaluates the joint action of all agents in the current global state, using slow target-network updates as in DDPG.A single policy computes actions for all agents, whose actions may differ because their histories differ.Source: §3.2 · p. 5
3.3 Distributed Local Actors
- Actor updates apply one shared local-history policy across agents and average its gradient contributions; improvement requires actor and Q-function gradients to be correlated.The policy can improve when histories contain enough information, in expectation, to determine a high-quality action for the given global state.Source: §3.3 · p. 6
3.4 Modeling Communication
- A fixed-dimensional distance histogram handles changing neighbor counts, using K radial basis functions centered evenly across the communication radius with σ = 0.25.The sensed distances are to neighboring agents within range; the histogram is used because the neural network input must have fixed dimensionality.Source: §3.4 · pp. 6–7
4 Experiments
The experiments test simulated, Kilobot-inspired agents on graph building and cooperative target localization, with policies evaluated across swarm sizes. Results show that learned performance depends on the number of agents involved in training, and that the guided approach outperformed tested joint-history learning on the edge task.
4.1 Simulation Environment
- Experimental simulations place agents in a 2D torus, represent the global state through individual agents’ local states plus an environment state, and set communication radius d = 4.Source: §4.1 · p. 7
4.2 Task 1: Building a Graph
- For the graph task, agents maximize edges between pairs whose distance is within the specified range, with rewards counting unique edges.The valid pairwise distance is 1.5 to 3.Source: §4.2 · pp. 7–8
4.3 Task 2: Localizing a Target
- For localization, agents cooperatively find a target; each agent’s localization bit changes from 0 to 1 when it finds the target.The reward counts agents that have located the target and includes an additional action punishment.Source: §4.3 · pp. 8–9
4.4 Hyper Parameter Setup
- The actor and critic use fully connected neural networks with four and three hidden layers, respectively; training uses replay batches of 32 and a 0.0001 base learning rate.The replay buffer holds 500,000 experience tuples.Source: §4.4 · p. 9
4.5 Results
- Experiments used four independent training trials per scenario, with 500-step episodes and a 10-step history horizon.The authors report that high deep-learning computational demands prevented more evaluations.Source: §4.5 · p. 9
- Policies trained with fewer agents perform better on smaller swarms, while those trained with more agents perform better on larger swarms; 7- and 8-agent policies do not match the 6-agent policy.In an 8-agent episode, the 3-agent-trained policy forms smaller groups, with 6 edges at t = 150; the 6-agent-trained policy forms one larger group, with around 20 edges at t = 350. Outcomes also become noisier as more agents learn together.Source: Figure 3 · p. 10, Figure 4 · p. 11, Task 1 · p. 11
4.6 Guided Learning vs. Standard Learning
- For the four-agent edge task, joint-history Q(h, a) learning failed to produce meaningful policies across tested architectures, whereas the guided approach was evaluated against those attempts.The authors suggest that the joint history grows substantially in dimensionality and that a 10-step history contains too little information about the global state.Source: Figure 7 · p. 13, §4.6 · p. 13
4.5 Results
- Policies trained with fewer than about four agents struggle to process information in larger scenarios, while sufficient participation lets communication-based policies outperform a policy without inter-agent communication.The resulting behavior keeps agents that have found the target near it.Source: Task 2 · p. 13, Figure 8 · p. 14
5 Conclusion
The conclusion presents guided policy learning for homogeneous swarms: a Q-function uses global state while actors act on local sensing, and the results indicate that non-guided learning is not feasible.
- The results indicate that the non-guided approach is not feasible, while guided learning produces distributed control policies with limited sensing.Source: §5 · p. 14
- The actor-critic approach learns the Q-function from global state while actors base decisions only on locally sensed information.Source: §5 · p. 14
- The authors see potential for global-state guidance to extend deep reinforcement learning to cooperative scenarios beyond current methods, with additional sensors as one way to acquire more training-state information.Source: §5 · p. 14