If someone consider adding a new model to SerpentAI... #10
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Some games may require Long and Short Term memory, such as strategy and MOBA games. However, as time passes during a gameplay, the initial states stored in memory can get quite noisy and the performance might be prejudiced. In the case of Dota 2, OpenAI Five used a single LSTM layer with 4096 units using a game state extracted from Dota Developer's API: However, I find the option of using a Developer's API as...lame. If someone is interested in trying it, then one could take a look at Overwolf API. They can send information from many games, including League of Legends, Valorant, Fortnite, Dota 2, Overwatch and there's even Eternal Return and Minecraft there. JavaScript publisher example with const redis = require("redis");
const redis_server = require('redis-server')
const server = new redis_server(6379)
server.open((err) => {
if (err === null) {
// You may now connect a client to the Redis
// server bound to port 6379.
}
});
const publisher = redis.createClient();
var channel = "game_events"
var i = 0
while(i < 10){
i++;
publisher.publish(channel, "testing");
}Python subscriber example. PS: This from redis import StrictRedis
client = StrictRedis(decode_responses=True)
subscriber = client.pubsub()
subscriber.psubscribe('game_events')
while True:
messages = subscriber.get_message()
if messages:
print(messages)However, DeepMind seems to have created a more interesting model, which was used to play StarCraft 2: AlphaStar. It uses LSTMs, discards the need of an API through using images and is possible to pre-train by visualizing replays of professional players. However, it's obviously more complex than a single LSTM layer with thousands of units. Here's some suggestions: |
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One that could be more data efficient and discard mighty datacenter hardwares would be interesting.
Suggestion: https://github.com/mila-iqia/spr
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