Automated Responses
Following our last guide, our creation has progressed from a simple bot to a sophisticated autonomous agent. Now, let's further enhance its capabilities by adding a counterattack feature, allowing it to instantly retaliate against an opponent's attack, potentially catching them off-guard before they can retreat to safety.
Writing the code
Add the following handler to your bot.lua file and you're set:
-- Handler to automatically attack when hit by another player.
Handlers.add(
"ReturnAttack",
Handlers.utils.hasMatchingTag("Action", "Hit"),
function (msg)
local playerEnergy = LatestGameState.Players[ao.id].energy
if playerEnergy == undefined then
print("Unable to read energy.")
ao.send({Target = Game, Action = "Attack-Failed", Reason = "Unable to read energy."})
elseif playerEnergy == 0 then
print("Player has insufficient energy.")
ao.send({Target = Game, Action = "Attack-Failed", Reason = "Player has no energy."})
else
print("Returning attack.")
ao.send({Target = Game, Action = "PlayerAttack", Player = ao.id, AttackEnergy = tostring(playerEnergy)})
end
InAction = false
ao.send({Target = ao.id, Action = "Tick"})
end
)
Whenever your player is under attack you receive a message with the Action Hit. This setup ensures your agent can make a swift counter attack, given it has sufficient energy.
You can refer to the latest code for bot.lua in the dropdown below:
Updated bot.lua file
LatestGameState = LatestGameState or nil
function inRange(x1, y1, x2, y2, range)
return math.abs(x1 - x2) <= range and math.abs(y1 - y2) <= range
end
function decideNextAction()
local player = LatestGameState.Players[ao.id]
local targetInRange = false
for target, state in pairs(LatestGameState.Players) do
if target ~= ao.id and inRange(player.x, player.y, state.x, state.y, 1) then
targetInRange = true
break
end
end
if player.energy > 5 and targetInRange then
print("Player in range. Attacking.")
ao.send({Target = Game, Action = "PlayerAttack", Player = ao.id, AttackEnergy = tostring(player.energy)})
else
print("No player in range or insufficient energy. Moving randomly.")
local directionMap = {"Up", "Down", "Left", "Right", "UpRight", "UpLeft", "DownRight", "DownLeft"}
local randomIndex = math.random(#directionMap)
ao.send({Target = Game, Action = "PlayerMove", Player = ao.id, Direction = directionMap[randomIndex]})
end
end
Handlers.add(
"HandleAnnouncements",
Handlers.utils.hasMatchingTag("Action", "Announcement"),
function (msg)
ao.send({Target = Game, Action = "GetGameState"})
print(msg.Event .. ": " .. msg.Data)
end
)
Handlers.add(
"UpdateGameState",
Handlers.utils.hasMatchingTag("Action", "GameState"),
function (msg)
local json = require("json")
LatestGameState = json.decode(msg.Data)
ao.send({Target = ao.id, Action = "UpdatedGameState"})
end
)
Handlers.add(
"decideNextAction",
Handlers.utils.hasMatchingTag("Action", "UpdatedGameState"),
function ()
if LatestGameState.GameMode ~= "Playing" then
return
end
print("Deciding next action.")
decideNextAction()
end
)
Handlers.add(
"ReturnAttack",
Handlers.utils.hasMatchingTag("Action", "Hit"),
function (msg)
local playerEnergy = LatestGameState.Players[ao.id].energy
if playerEnergy == undefined then
print("Unable to read energy.")
ao.send({Target = Game, Action = "Attack-Failed", Reason = "Unable to read energy."})
elseif playerEnergy == 0 then
print("Player has insufficient energy.")
ao.send({Target = Game, Action = "Attack-Failed", Reason = "Player has no energy."})
else
print("Returning attack.")
ao.send({Target = Game, Action = "PlayerAttack", Player = ao.id, AttackEnergy = tostring(playerEnergy)})
end
InAction = false
ao.send({Target = ao.id, Action = "Tick"})
end
)
Loading and Testing
To activate and test the counter attack feature, load the bot file in your aos player terminal:
.load bot.lua
Watch your terminal for the autonomous agent's reactions, now with the added ability to retaliate instantly. This feature showcases the agent's evolving strategic depth and autonomy. In the upcoming section, we'll consolidate all the knowledge we've gathered so far and add some features for optimization.