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Title: Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

Paper content:
Learning when to communicate and doing that effectively is essential in multi-agent tasks.
Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks.
In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings.
IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues.
Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases.
Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.
Communication is an essential element of intelligence as it helps in learning from others experience, work better in teams and pass down knowledge.
In multi-agent settings, communication allows agents to cooperate towards common goals.
Particularly in partially observable environments, when the agents are observing different parts of the environment, they can share information and learnings from their observation through communication.
Recently, there have been a lot of success in the field of reinforcement learning (RL) in playing Atari Games BID22 to playing Go BID28 , most of which have been limited to the single agent domain.
However, the number of systems and applications having multi-agents have been growing BID16 BID23 ; where size can be from a team of robots working in manufacturing plants to a network of self-driving cars.
Thus, it is crucial to successfully scale RL to multi-agent environments in order to build intelligent systems capable of higher productivity.
Furthermore, scenarios other than cooperative, namely semi-cooperative (or mixed) and competitive scenarios have not even been studied as extensively for multi-agent systems.
The mixed scenarios can be compared to most of the real life scenarios as humans are cooperative but not fully-cooperative in nature.
Humans work towards their individual goals while cooperating with each other.
In competitive scenarios, agents are essentially competing with each other for better rewards.
In real life, humans always have an option to communicate but can choose when to actually communicate.
For example, in a sports match two teams which can communicate, can choose to not communicate at all (to prevent sharing strategies) or use dishonest signaling (to misdirect opponents) BID18 in order to optimize their own reward and handicap opponents; making it important to learn when to communicate.
Teaching agents how to communicate makes it is unnecessary to hand code the communication protocol with expert knowledge BID29 BID14 .
While the content of communication is important, it is also important to know when to communicate either to increase scalability and performance or to increase competitive edge.
For example, a prey needs to learn when to communicate to avoid communicating its location with predators.
BID29 showed that agents communicating through a continuous vector are easier to train and have a higher information throughput than communication based on discrete symbols.
Their continuous communication is differentiable, so it can be trained efficiently with back-propagation.
However, their model assumes full-cooperation between agents and uses average global rewards.
This restricts the model from being used in mixed or competitive scenarios as full-cooperation involves sharing hidden states to everyone; exposing everything and leading to poor performance by all agents as shown by our results.
Furthermore, the average global reward for all agents makes the credit assignment problem even harder and difficult to scale as agents don't know their individual contributions in mixed or competitive scenarios where they want themselves to succeed before others.
To solve above mentioned issues, we make the following contributions:1.
We propose Individualized Controlled Continuous Communication Model (IC3Net), in which each agent is trained with its individualized reward and can be applied to any scenario whether cooperative or not.
2.
We empirically show that based on the given scenario-using the gating mechanism-our model can learn when to communicate.
The gating mechanism allows agents to block their communication; which is useful in competitive scenarios.
3.
We conduct experiments on different scales in three chosen environments including StarCraft and show that IC3Net outperforms the baselines with performance gaps that increase with scale.
The results show that individual rewards converge faster and better than global rewards.
The simplest approach in multi-agent reinforcement learning (MARL) settings is to use an independent controller for each agent.
This was attempted with Q-learning in BID30 .
However, in practice it performs poorly BID21 , which we also show in comparison with our model.
The major issue with this approach is that due to multiple agents, the stationarity of the environment is lost and naïve application of experience replay doesn't work well.
The nature of interaction between agents can either be cooperative, competitive, or a mix of both.
Most algorithms are designed only for a particular nature of interaction, mainly cooperative settings BID24 BID15 BID20 , with strategies which indirectly arrive at cooperation via sharing policy parameters BID9 .
These algorithms are generally not applicable in competitive or mixed settings.
See BID4 for survey of MARL in general and BID26 for survey of cooperative multi-agent learning.
Our work can be considered as an all-scenario extension of BID29 's CommNet for collaboration among multiple agents using continuous communication; usable only in cooperative settings as stated in their work and shown by our experiments.
Due to continuous communication, the controller can be learned via backpropagation.
However, this model is restricted to fully cooperative tasks as hidden states are fully communicated to others which exposes everything about agent.
On the other hand, due to global reward for all agents, CommNet also suffers from credit assignment issue.
The Multi-Agent Deep Deterministic Policy Gradient (MADDPG) model presented by BID19 also tries to achieve similar goals.
However, they differ in the way of providing the coordination signal.
In their case, there is no direct communication among agents (actors with different policy per agent), instead a different centralized critic per agent -which can access the actions of all the agentsprovides the signal.
Concurrently, a similar model using centralized critic and decentralized actors with additional counterfactual reward, COMA by BID8 was proposed to tackle the challenge of multiagent credit assignment by letting agents know their individual contributions.
Vertex Attention Interaction Networks (VAIN) BID13 ) also models multi-agent communication through the use of Interaction Networks BID1 with attention mechanism BID0 for predictive modelling using supervised settings.
In contrast, a lot of work has focused on understanding agents' communication content; mostly in discrete settings with two agents BID33 BID10 BID14 BID16 BID17 .
BID16 showed that given two neural network agents and a referential game, the agents learn to coordinate.
BID10 extended this by grounding communication protocol to a symbols's sequence while BID14 showed that this language can be made more human-like by placing certain restrictions.
BID17 DISPLAYFORM0 where o t j is the observation of the j-th agent at time t, e(·) is an encoder function parameterized by a fully-connected neural network and π is an agent's action policy.
Also, h t j and s t j are the hidden and cell states of the LSTM.
We use the same LSTM model for all agents, sharing their parameters.
This way, the model is invariant to permutations of the agents.
IC3Net extends this independent controller model by allowing agents to communicate their internal state, gated by a discrete action.
The policy of the j-th agent in a IC3Net is given by DISPLAYFORM1 where c t j is the communication vector for the j-th agent, C is a linear transformation matrix for transforming gated average hidden state to a communication tensor, J is the number of alive agents currently present in the system and f g (.) is a simple network containing a soft-max layer for 2 actions (communicate or not) on top of a linear layer with non-linearity.
The binary action g t j specifies whether agent j wants to communicate with others, and act as a gating function when calculating the communication vector.
Note that the gating action for next time-step is calculated at current time-step.
We train both the action policy π and the gating function f g with REINFORCE (Williams, 1992).In BID29 , individual networks controlling agents were interconnected, and they as a whole were considered as a single big neural network.
This single big network controller approach required a definition of an unified loss function during training, thus making it impossible to train agents with different rewards.
In this work, however, we move away from the single big network controller approach.
Instead, we consider multiple big networks with shared parameters each controlling a single agent separately.
Each big network consists of multiple LSTM networks, each processing an observation of a single agent.
However, only one of the LSTMs need to output an action because the big network is only controlling a single agent.
Although this view has a little effect on the implementation (we can still use a single big network in practice), it allows us to train each agent to maximize its individual reward instead of a single global reward.
This has two benefits: (i) it allows the model to be applied to both cooperative and competitive scenarios, (ii) it also helps resolve the credit assignment issue faced by many multi-agent BID29 BID6 algorithms while improving performance with scalability and is coherent with the findings in BID5 .
We study our network in multi-agent cooperative, mixed and competitive scenarios to understand its workings.
We perform experiments to answer following questions:1.
Can our network learn the gating mechanism to communicate only when needed according to the given scenario?
Essentially, is it possible to learn when to communicate?
2.
Does our network using individual rewards scales better and faster than the baselines?
This would clarify, whether or not, individual rewards perform better than global rewards in multi-agent communication based settings.
We first analyze gating action's (g t ) working.
Later, we train our network in three chosen environments with variations in difficulty and coordination to ensure scalability and performance.
We consider three environments for our analysis and experiments.
tasks which test control on multiple agents in various scenarios where agent needs to understand and decouple observations for multiple opposing units.
In this task, we have n predators (agents) with limited vision trying to find a stationary prey.
Once a predator reaches a prey, it stays there and always gets a positive reward, until end of episode (rest of the predators reach prey, or maximum number of steps).
In case of zero vision, agents don't have a direct way of knowing prey's location unless they jump on it.
We design three cooperation settings (competitive, mixed and cooperative) for this task with different reward structures to test our network.
See Appendix 6.3 for details on grid, reward structure, observation and action space.
There is no loss or benefit from communicating in mixed scenario.
In competitive setting, agents get lower rewards if other agents reach the prey and in cooperative setting, reward increases as more agents reach the prey.
We compare with baselines using mixed settings in subsection 4.3.2 while explicitly learning and analyzing gating action's working in subsection 4.2.We create three levels for this environment -as mentioned in Appendix 6.3 -to compare our network's performance with increasing number of agents and grid size.
10×10 grid version with 5 agents is shown in FIG0 (left).
All agents are randomly placed in the grid at start of an episode.
Following BID29 , we test our model on the traffic junction task as it is a good proxy for testing whether communication is working.
This task also helps in supporting our claim that IC3Net provides good performance and faster convergence in fully-cooperative scenarios similar to mixed ones.
In the traffic junction, cars enter a junction from all entry points with a probability p arr .
The maximum number of cars at any given time in the junction is limited.
Cars can take two actions at each time-step, gas and brake respectively.
The task has three difficulty levels (see FIG0 ) which vary in the number of possible routes, entry points and junctions.
We make this task harder by always setting vision to zero in all the three difficulty levels to ensure that task is not solvable without communication.
See Appendix 6.4 for details on reward structure, observation and training.
To fully understand the scalability of our architecture in more realistic and complex scenarios, we test it on StarCraft combat and exploration micro-management tasks in partially observable settings.
StarCraft is a challenging environment for RL because it has a large observation-action space, many different unit types and stochasticity.
We train our network on Combat and Explore task.
The task's difficulty can be altered by changing the number of our units, enemy units and the map size.
In cooperative setting (a, e) agent almost always communicate to increase their own reward.
In (b) mixed setting and (c) competitive setting, predators only communicate when necessary and profitable.
As is evident from (f), they stop communicating once they reach prey.
In all cases, prey almost never communicates with predators as it is not profitable for it.
Similarly, in competitive scenario (d) for SC, team agents learn to communicate only when necessary due to the division of reward when near enemy, while enemy agent learns not to communicate as in PP.By default, the game has macro-actions which allow a player to directly target an enemy unit which makes player's unit find the best possible path using the game's in-built path-finding system, move towards the target and attack when it is in a range.
However, we make the task harder by (i) removing macro-actions making exploration harder (ii) limiting vision making environment partially observable(iii) unlike previous works BID34 BID25 BID32 BID27 , initializing enemy and our units at random locations in a fixed size square on the map, which makes it challenging to find enemy units.
Refer to Appendix 6.5.1 for reward, action, observation and task details.
We consider two types of tasks in StarCraft:
Explore: In this task, we have n agents trying to explore the map and find an enemy unit.
This is a direct scale-up of the PP but with more realistic and stochastic situations.
Combat: We test an agent's capability to execute a complex task of combat in StarCraft which require coordination between teammates, exploration of a terrain, understanding of enemy units and formalism of complex strategies.
We specifically test a team of n agents trying to find and kill a team of m enemies in a partially observable environment similar to the explore task.
The agents, with their limited vision, must find the enemy units and kill all of them to score a win.
More information on reward structure, observation and setup can be found in Appendix 6.5.1 and 6.5.2.
We analyze working of gating action (g t ) in IC3Net by using cooperative, competitive and mixed settings in Predator-Prey (4.1.1) and StarCraft explore tasks (4.1.3).
However, this time the enemy unit (prey) shares parameters with the predators and is trained with them.
All of the enemy unit's actions are noop which makes it stationary.
The enemy unit gets a positive reward equivalent to r time = 0.05 per timestep until no predator/medic is captures it; after that it gets a reward of 0.For 5×5 grid in PP task, FIG1 shows gating action (averaged per epoch) in all scenarios for (i) communication between predator and (ii) communication between prey and predators.
We also test on 50×50 map size for competitive and cooperative StaraCraft explore task and found similar results FIG1 .
We can deduce following observations:• As can be observed in FIG1 , 3b, 3c and 3d, in all the four cases, the prey learns not to communicate.
If the prey communicates, predators will reach it faster.
Since it will get 0 reward when an agent comes near or on top of it, it doesn't communicate to achieve higher rewards.• In cooperative setting FIG1 , the predators are openly communicating with g close to 1.
Even though the prey communicates with the predators at the start, it eventually learns not to communicate; so as not to share its location.
As all agents are communicating in this setting, it takes more training time to adjust prey's weights towards silence.
Our preliminary tests suggest that in cooperative settings, it is beneficial to fix the gating action to 1.0 as communication is almost always needed and it helps in faster training by skipping the need to train the gating action.• In the mixed setting FIG1 ), agents don't always communicate which corresponds to the fact that there is no benefit or loss by communicating in mixed scenario.
The prey is easily able to learn not to communicate as the weights for predators are also adjusted towards non-cooperation from the start itself.• As expected due to competition, predators rarely communicate in competitive setting FIG1 , 3d).
Note that, this setting is not fully-adversarial as predators can initially explore faster if they communicate which can eventually lead to overall higher rewards.
This can be observed as the agents only communicate while it's profitable for them, i.e. before reaching the prey FIG1 )) as communicating afterwards can impact their future rewards.
Experiments in this section, empirically suggest that agents can "learn to communicate when it is profitable"; thus allowing same network to be used in all settings.
In this section, we look at bigger versions of our environments to understand scalability and generalization aspects of IC3Net.
For training details, refer to Appendix 6.1.
We compare IC3Net with baselines specified below in all scenarios.
In this controller, model is applied individually to all of the agents' observations to produce the action to be taken.
Essentially, this can be seen as IC3Net without any communication between agents; but with individualized reward for each agent.
Note that no communication makes gating action (g t ) ineffective.
Like IRIC except the agents are trained with a global average reward instead of individual rewards.
This will help us understand the credit assignment issue prevalent in CommNet.
CommNet: Introduced in BID29 , CommNet allows communication between agents over a channel where an agent is provided with the average of hidden state representations of other agents as a communication signal.
Like IC3Net, CommNet also uses continuous signals to communicate between the agents.
Thus, CommNet can be considered as IC3Net without both the gating action (g t ) and individualized rewards.
We discuss major results for our experiments in this section and analyze particular behaviors/patterns of agents in Appendix 6.2.Predator Prey: Table 1 (left) shows average steps taken by the models to complete an episode i.e. find the prey in mixed setting (we found similar results for cooperative setting shown in appendix).
IC3Net reaches prey faster than the baselines as we increase the number of agents as well as the size of the maze.
In 20×20 version, the gap in average steps is almost 24 steps, which is a substantial improvement over baselines.
FIG2 (right) shows the scalability graph for IC3Net and CommNet which supports the claim that with the increasing number of agents, IC3Net converges faster at a better optimum than CommNet.
Through these results on the PP task, we can see that compared to IC3Net, CommNet doesn't work well in mixed scenarios.
Finally, FIG2 (left) shows the training plot of 20×20 grid with 10 agents trying to find a prey.
The plot clearly shows the faster performance improvement of IC3Net in contrast to CommNet which takes long time to achieve a minor jump.
We also find same pattern of the gating action values as in 4.2.Traffic Junction: Table 1 (right) shows the success ratio for traffic junction.
We fixed the gating action to 1 for TJ as discussed in 4.2.
With zero vision, it is not possible to perform well without communication as evident by the results of IRIC and IC.
Interestingly, IC performs better than IRIC in the hard case, as we believe without communication, the global reward in TJ acts as a better indicator of the overall performance.
On the other hand, with communication and better knowledge of others, the global reward training face a credit assignment issue which is alleviated by IC3Net as evident by its superior performance compared to CommNet.
In BID29 , well-performing agents in the medium and hard versions had vision > 0.
With zero vision, IC3Net is to CommNet and IRIC with a performance gap greater than 30%.
This verifies that individualized rewards in IC3Net help achieve a better or similar performance than CommNet in fully-cooperative tasks with communication due to a better credit assignment.
ment on reverse scenario where our agents control 3 Zealots initialized separately and enemies are 10 marines initialized together.
We find that both IRIC and IC3Net reach a success percentage of 100% easily.
We find that even in this case, IC3Net converges faster than IRIC.
In this work, we introduced IC3Net which aims to solve multi-agent tasks in various cooperation settings by learning when to communicate.
Its continuous communication enables efficient training by backpropagation, while the discrete gating trained by reinforcement learning along with individual rewards allows it to be used in all scenarios and on larger scale.
Through our experiments, we show that IC3Net performs well in cooperative, mixed or competitive settings and learns to communicate only when necessary.
Further, we show that agents learn to stop communication in competitive cases.
We show scalability of our network by further experiments.
In future, we would like to explore possibility of having multi-channel communication where agents can decide on which channel they want to put their information similar to communication groups but dynamic.
It would be interesting to provide agents a choice of whether to listen to communication from a channel or not.
We set the hidden layer size to 128 units and we use LSTM BID12 with recurrence for all of the baselines and IC3Net.
We use RMSProp BID31 with initial learning rate as a tuned hyper-parameter.
All of the models use skip-connections BID11 .
The training is distributed over 16 cores and each core runs a mini-batch till total episodes steps are 500 or more.
We do 10 weight updates per epoch.
We run predator-prey, StarCraft experiments for 1000 epochs, traffic junction experiment for 2000 epochs and report the final results.
In mixed case, we report the mean score of all agents, while in cooperative case we report any agent's score as they are same.
We implement our model using PyTorch and environments using Gym BID3 .We use REINFORCE (Williams, 1992) to train our setup.
We conduct 5 runs on each of the tasks to compile our results.
The training time for different tasks varies; StarCraft tasks usually takes more than a day (depends on number of agents and enemies), while predator-prey and traffic junction tasks complete under 12 hours.
In this section, we analyze and discuss behaviors/patterns in the results on our experiments.
As observed in TAB3 , IC3Net performs better than CommNet in explore task but doesn't outperform it on Combat task.
Our experiments and visualizations of actual strategy suggested that compared to exploration, combat can be solved far easily if the units learn to stay together.
Focused firepower with more attack quantity in general results in quite good results on combat.
We verify this hypothesis by running a heuristics baseline "attack closest" in which agents have full vision on map and have macro actions available 3 .
By attacking the closest available enemy together the agents are able to kill zealots with success ratio of 76.6± 8 calculated over 5 runs, even though initialized separately.
Also, as described in Appendix 6.5.2, the global reward in case of win in Combat task is relatively huge compared to the individual rewards for killing other units.
We believe that with coordination to stay together, huge global rewards and focus fire-which is achievable through simple cooperation-add up to CommNet's performance in this task.
Further, in exploration we have seen that agents go in separate direction and have individual rewards/sense of exploration which usually leads to faster exploration of an unexplored area.
Thinking in simple terms, exploration of an house would be faster if different people handle different rooms.
Achieving this is hard in CommNet because global rewards don't exactly tell your individual contributions if you had explored separately.
Also in CommNet, we have observed that agents follow a pattern where they get together at a point and explore together from that point which further signals that using CommNet, it is easy to get together for agents 4 .
In FIG3 , we have observed significant variance in IC3Net results for StarCraft.
We performed a lot of experiments on StarCraft and can attribute the significant variance to stochasticity in the environment.
There are a huge number of possible states in which agents can end up due to millions of possible interactions and their results in StarCraft.
We believe it is hard to learn each one of them.
This stochasticity variance can even be seen in simple heuristics baselines like "attack closest" (6.2.1) and is in-fact an indicator of how difficult is it to learn real-world scenarios which also have same amount of stochasticity.
We believe that we don't see similar variance in CommNet and other baselines because adding gating action increases the action-state-space combinations which yields better results while being difficult to learn sometimes.
Further, this variance is only observed in higher Win % models which requires to learn more state spaces.
In TAB3 , we can observe that CommNet performs worse than IRIC and IC in case of StarCraftExplore task.
In this section, we provide a hypothesis for this result.
First, we need to notice is that IRIC is better than IC also overall, which points to the fact that individualized reward are better than global rewards in case of exploration.
This makes sense because if agents cover more area and know how much they covered through their own contribution (individual reward), it should lead to overall more coverage, compared to global rewards where agents can't figure out their own coverage but instead overall one.
Second, in case of CommNet, it is easy to communicate and get together.
We observe this pattern in CommNet 4 where agents first get together at a point and then start exploring from there which leads to slow exploration, but IC is better in this respect because it is hard to gather at single point which inherently leads to faster exploration than CommNet.
Third, the reward structure in the case of mixed scenario doesn't appreciate searching together which is not directly visible to CommNet and IC due to global rewards.
In all the three settings, cooperative, competitive and mixed, a predator agent gets a constant time-step penalty r explore = −0.05, until it reaches the prey.
This makes sure that agent doesn't slack in finding the prey.
In the mixed setting, once an agent reaches the prey, the agent always gets a positive reward r prey = 0.05 which doesn't depend on the number of agents on prey. .
Similarly, in the cooperative setting, an agent gets a positive reward of r coop = r prey * n, and in the competitive setting, an agent gets a positive reward of r comp = r prey / n after it reaches the prey, where n is the number of agents on the prey.
The total reward at time t for an agent i can be written as: r pp i (t) = δ i * r explore + (1 − δ i ) * n λ t * r prey * |λ| where δ i denotes whether agent i has found the prey or not, n t is number of agents on prey at time-step t and λ is -1, 0 and 1 in the competitive, mixed and cooperative scenarios respectively.
Maximum episode steps are set to 20, 40 and 80 for 5×5, 10×10 and 20×20 grids respectively.
The number of predators are 5, 10 and 20 in 5×5, 10×10 and 20×20 grids respectively.
Each predator can take one of the five basic movement actions i.e. up, down, lef t, right or stay.
Predator, prey and all locations on grid are considered unique classes in vocabulary and are represented as one-hot binary vectors.
Observation obs, at each point will be the sum of all one-hot binary vectors of location, predators and prey present at that point.
With vision of 1, observation of each agent have dimension 3 2 × |obs|.
Table 3 : Predator-Prey Cooperative:
Avg. rewards in three difficulty levels of predator-prey environment in cooperative setting.
IC3Net performs equivalently or better than baselines consistently.
Table 3 shows the results for IC3Net and baselines in the cooperative scenario for the predator-prey environment.
As the cooperative reward function provides more reward after a predator reaches the prey, the comparison is provided for rewards instead of average number of steps.
IC3Net performs better or equal to CommNet and other baselines in all three difficulty levels.
The performance gap closes in and increases as we move towards bigger grids which shows that IC3Net is more scalable due to individualized rewards.
More importantly, even with the extra gating action training, IC3Net can perform comparably to CommNet which is designed for cooperative scenarios which suggests that IC3Net is a suitable choice for all cooperation settings.
To analyze the effect of gating action on rewards in case of mixed scenario where individualized rewards alone can help a lot, we test Predator Prey mixed cooperation setting on 20x20 grid on a baseline in which we set gating action to 1 (global communication) and uses individual rewards (IC2Net/CommNet + IR).
We find average max steps to be 50.24± 3.4 which is lower than IC3Net.
This means that (i) individualized rewards help a lot in mixed scenarios by allowing agents to understand there contributions (ii) adding the gating action in this case has an overhead but allows the same model to work in all settings (even competitive) by "learning to communicate" which is more close to real-world humans with a negligible hit on the performance.
Traffic junction's observation vocabulary has one-hot vectors for all locations in the grid and car class.
Each agent observes its previous action, route identifier and a vector specifying sum of one-hot vectors for all classes present at that agent's location.
Collision occurs when two cars are on same location.
We set maximum number of steps to 20, 40 and 60 in easy, medium and hard difficulty respectively.
Similar to BID29 , we provide a negative reward r coll = -10 on collision.
To cut off traffic jams, we provide a negative reward τ i r time = -0.01 τ i where τ i is time spent by the agent in the junction at time-step t. Reward for ith agent which is having C t i collisions at time-step t can be written as: DISPLAYFORM0 We utilized curriculum learning BID2 to make the training process easier.
The p arrive is kept at the start value till the first 250 epochs and is then linearly increased till the end value during the course from 250th to 1250th epoch.
The start and end values of p arrive for different difficulty levels are indicated in TAB7 .
Finally, training continues for another 750 epochs.
The learning rate is fixed at 0.003 throughout.
We also implemented three difficulty variations of the game explained as follows.
The easy version is a junction of two one-way roads on a 7 × 7 grid.
There are two arrival points, each with two possible routes and with a N total value of 5.The medium version consists of two connected junctions of two-way roads in 14 × 14 as shown in FIG0 (right).
There are 4 arrival points and 3 different routes for each arrival point and have N total = 20.The harder version consists of four connected junctions of two-way roads in 18 × 18 as shown in Figure 6 .
There are 8 arrival points and 7 different routes for each arrival point and have N total = 20.
In Table 1 , we notice that IRIC and IC perform worst in medium level compared to the hard level.
Our visualizations suggest that this is due to high final add-rate in case of medium version compared to hard version.
Collisions happen much more often in medium version leading to less success rate (an episode is considered failure if a collision happens) compared to hard where initial add-rate is low to accommodate curriculum learning for hard version's big grid size.
The final add-rate in case of hard level is comparatively low to make sure that it is possible to pass a junction without a collision as with more entry points it is easy to collide even with a small add-rate.
Figure 6 : Hard difficulty level of traffic junction task.
Level has four connected junctions, eight entry points and at each entry point there are 7 possible routes increasing chances of a collision.
We use curriculum learning to successfully train our models on hard level.6.5 STARCRAFT DETAILS
Explore: To complete the explore task, agents must be within a particular range of enemy unit called explore vision.
Once an agent is within explore vision of enemy unit, we noop further actions.
The reward structure is same as the PP task with only difference being that an agent needs to be within the explore vision range of the enemy unit instead of being on same location to get a non-negative reward.
We use medic units which don't attack enemy units.
This ensures that we can simulate our explore task without any of kind of combat happening and interfering with the goal of the task.
Observation for each agent is its (absolute x, absolute y) and enemy's (relative x, relative y, visible) where visible, relative x and relative y are 0 when enemy is not in explore vision range.
Agents have 9 actions to choose from which includes 8 basic directions and one stay action.
Combat: Agent observes its own (absolute x, absolute y, healthpoints + shield, weapon cooldown, previous action) and (relative x, relative y, visible, healthpoints + shield, weapon cooldown) for each of the enemies.
relative x and relative y are only observed when enemy is visible which is corresponded by visible flag.
All of the observations are normalized to be in between (0, 1).
Agent has to choose from 9 + m actions which include 9 basic actions and 1 action for attacking each of the m agents.
Attack actions only work when the enemy is within the sight range of the agent, otherwise it is a noop.
In combat, we don't compare with prior work on StarCraft because our environment setting is much harder, restrictive, new and different, thus, not directly comparable.
To avoid slack in finding the enemy team, we provide a negative reward r time = -0.01 at each timestep when the agent is not involved in a combat.
At each timestep, an agent gets as reward the difference between (i) its normalized health in current and previous timestep (ii) normalized health at previous timestep and current timestep for each of the enemies it has attacked till now.
At the end of the episode, terminal reward for each agents consists of (i) all its remaining health * 3 as negative reward (ii) 5 * m + all its remaining health * 3 as positive reward if agents win (iii) normalized remaining health * 3 for all of the alive enemies as negative reward on lose.
In this task, the group of enemies is initialized together randomly in one half of the map and our agents are initialized separately in other half which makes task even harder, thus requiring communication.
For an automatic way of individualizing rewards, please refer to BID8 .
We provide an example sequence of states in StarCraft cooperative explore mode in Figure 7 .
As soon as one of the agents finds the enemy unit, the other agents get the information about enemy's location through communication and are able to reach it faster.
Figure 7: State sequence in SC Cooperative Explore.
We can see how agents which are randomly initialized communicate to reach the enemy faster.
Once, some agents are near the prey and other reach very fast due to communication

assistant: text: Author proposes a new architecture for multi-agent reinforcement learning that uses several LSTM controllers with tied weights that transmit a continuous vector to each other