base evolution model. needs different memory handling
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zomseffen
parent
33b5d9c83e
commit
4a05baa103
4 changed files with 218 additions and 48 deletions
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@ -146,7 +146,8 @@ class LabyrinthWorld(World):
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# adding subjects
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from labirinth_ai.Subject import Hunter, Herbivore
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while len(self.subjects) < 2:
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for _ in range(10):
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while True:
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px = random.randint(self.max_room_dim, self.board_shape[0] - self.max_room_dim)
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py = random.randint(self.max_room_dim, self.board_shape[1] - self.max_room_dim)
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if self.board[px, py] == 1:
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@ -154,8 +155,10 @@ class LabyrinthWorld(World):
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self.ins += self.subjects[-1].x_in
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self.actions += self.subjects[-1].actions
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self.targets += self.subjects[-1].target
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break
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while len(self.subjects) < 10:
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for _ in range(40):
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while True:
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px = random.randint(self.max_room_dim, self.board_shape[0] - self.max_room_dim)
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py = random.randint(self.max_room_dim, self.board_shape[1] - self.max_room_dim)
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if self.board[px, py] == 1:
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@ -163,6 +166,7 @@ class LabyrinthWorld(World):
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self.ins += self.subjects[-1].x_in
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self.actions += self.subjects[-1].actions
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self.targets += self.subjects[-1].target
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break
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for x in range(self.board_shape[0]):
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for y in range(self.board_shape[1]):
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@ -173,36 +177,14 @@ class LabyrinthWorld(World):
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def update(self):
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# start = time.time()
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if self.model is None:
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for sub in self.subjects:
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sub.calculateAction(self)
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else:
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states = list(map(lambda e: e.createState(self), self.subjects))
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states = sum(list(map(lambda e: [e, e, e, e], states)), [])
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vals = self.model.predict(states)
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vals = np.reshape(np.transpose(np.reshape(vals, (len(self.subjects), 4, 2)), (0, 2, 1)),
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(len(self.subjects), 1, 8))
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list(map(lambda e: e[1].calculateAction(self, vals[e[0]], states[e[0]]), enumerate(self.subjects)))
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for sub in self.subjects:
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if sub.alive:
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sub.update(self, doTrain=self.model is None)
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sub.update(self)
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sub.tick += 1
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if self.model is not None:
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if self.round >= self.nextTrain:
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samples = list(map(lambda e: e.generateSamples(), self.subjects))
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states = sum(list(map(lambda e: e[0], samples)), [])
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targets = sum(list(map(lambda e: e[1], samples)), [])
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self.model.fit(states, targets)
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self.nextTrain = self.batchsize / 5
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self.round = 0
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for sub in self.subjects:
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if len(sub.samples) > 20*self.batchsize:
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sub.samples = sub.samples[:-20*self.batchsize]
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else:
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self.round += 1
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new_subjects = []
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kill_table = {}
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live_table = {}
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@ -13,6 +13,7 @@ print(f"Using {device} device")
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# Define model
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class BaseModel(nn.Module):
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evolutionary = False
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def __init__(self, view_dimension, action_num, channels):
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super(BaseModel, self).__init__()
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self.flatten = nn.Flatten()
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@ -39,6 +40,7 @@ class BaseModel(nn.Module):
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actions.append(self.actions[action](x_flat))
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return torch.stack(actions, dim=1)
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class BaseDataSet(Dataset):
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def __init__(self, states, targets):
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assert len(states) == len(targets), "Needs to have as many states as targets!"
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@ -87,7 +89,7 @@ def train(states, targets, model, optimizer):
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# Backpropagation
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optimizer.zero_grad()
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loss.backward()
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loss.backward(retain_graph=True)
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optimizer.step()
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if batch % 100 == 0:
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@ -100,7 +102,7 @@ def train(states, targets, model, optimizer):
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if __name__ == '__main__':
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sample = np.random.random((1, 4, 11, 11))
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sample = np.random.random((1, 486))
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model = BaseModel(5, 4, 4).to(device)
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print(model)
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@ -109,7 +111,7 @@ if __name__ == '__main__':
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# test = test.cpu().detach().numpy()
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print(test)
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state = np.random.random((4, 11, 11))
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state = np.random.random((486,))
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target = np.random.random((4, 2))
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states = [
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[state],
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176
labirinth_ai/Models/EvolutionModel.py
Normal file
176
labirinth_ai/Models/EvolutionModel.py
Normal file
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@ -0,0 +1,176 @@
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import torch
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from torch import nn
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import numpy as np
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import tqdm
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from torch.utils.data import Dataset, DataLoader
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from labirinth_ai.Models.BaseModel import device
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class NodeGene:
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valid_types = ['sensor', 'hidden', 'output']
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def __init__(self, node_id, node_type, bias=None):
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assert node_type in self.valid_types, 'Unknown node type!'
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self.node_id = node_id
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self.node_type = node_type
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if node_type == 'hidden':
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assert bias is not None, 'Expected a bias for hidden node types!'
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self.bias = bias
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else:
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self.bias = None
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class ConnectionGene:
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def __init__(self, start, end, enabled, innovation_num, weight=None, recurrent=False):
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self.start = start
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self.end = end
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self.enabled = enabled
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self.innvovation_num = innovation_num
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self.recurrent = recurrent
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if weight is None:
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self.weight = np.random.random(1)[0] * 2 - 1.0
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else:
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self.weight = weight
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class EvolutionModel(nn.Module):
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evolutionary = True
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def __init__(self, view_dimension, action_num, channels, genes=None):
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super(EvolutionModel, self).__init__()
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self.flatten = nn.Flatten()
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self.action_num = action_num
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self.viewD = view_dimension
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self.channels = channels
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if genes is None:
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self.num_input_nodes = channels * (2 * self.viewD + 1) * (2 * self.viewD + 1) + 2
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self.genes = {'nodes': {}, 'connections': []}
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node_id = 0
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for _ in range(self.num_input_nodes):
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self.genes['nodes'][node_id] = NodeGene(node_id, 'sensor')
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node_id += 1
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first_action = node_id
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for _ in range(action_num * 2):
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self.genes['nodes'][node_id] = NodeGene(node_id, 'output')
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node_id += 1
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for index in range(self.num_input_nodes):
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for action in range(action_num * 2):
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self.genes['connections'].append(
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ConnectionGene(index, first_action + action, True, index*(action_num * 2) + action)
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)
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self.incoming_connections = {}
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for connection in self.genes['connections']:
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if connection.end not in self.incoming_connections.keys():
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self.incoming_connections[connection.end] = []
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self.incoming_connections[connection.end].append(connection)
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self.layers = {}
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self.indices = {}
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self.has_recurrent = False
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non_recurrent_indices = {}
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with torch.no_grad():
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for key, value in self.incoming_connections.items():
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value.sort(key=lambda element: element.start)
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lin = nn.Linear(len(value), 1, bias=self.genes['nodes'][key].bias is not None)
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for index, connection in enumerate(value):
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lin.weight[0, index] = value[index].weight
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if self.genes['nodes'][key].bias is not None:
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lin.bias[0] = self.genes['nodes'][key].bias
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non_lin = nn.ELU()
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sequence = nn.Sequential(
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lin,
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non_lin
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)
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self.add_module('layer_' + str(key), sequence)
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self.layers[key] = sequence
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self.indices[key] = list(map(lambda element: element.start, value))
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non_recurrent_indices[key] = list(filter(lambda element: not element.recurrent, value))
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if not self.has_recurrent and len(non_recurrent_indices[key]) != len(self.indices[key]):
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self.has_recurrent = True
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non_recurrent_indices[key] = list(map(lambda element: element.start, non_recurrent_indices[key]))
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rank_of_node = {}
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for i in range(self.num_input_nodes):
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rank_of_node[i] = 0
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layers_to_add = list(non_recurrent_indices.items())
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while len(layers_to_add) > 0:
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for index, (key, incoming_nodes) in enumerate(list(layers_to_add)):
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max_rank = -1
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all_ranks_found = True
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for incoming_node in incoming_nodes:
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if incoming_node in rank_of_node.keys():
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max_rank = max(max_rank, rank_of_node[incoming_node])
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else:
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all_ranks_found = False
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if all_ranks_found:
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rank_of_node[key] = max_rank + 1
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layers_to_add = list(filter(lambda element: element[0] not in rank_of_node.keys(), layers_to_add))
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ranked_layers = list(rank_of_node.items())
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ranked_layers.sort(key=lambda element: element[1])
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ranked_layers = list(filter(lambda element: element[1] > 0, ranked_layers))
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self.layer_order = list(map(lambda element: element[0], ranked_layers))
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self.memory = torch.Tensor((max(map(lambda element: element[1].node_id, self.genes['nodes'].items())) + 1))
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def forward(self, x, memory=None):
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x_flat = self.flatten(x)
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if memory is None:
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memory = torch.Tensor(self.memory)
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outs = []
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for batch_element in x_flat:
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memory[0:self.num_input_nodes] = batch_element
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for layer_index in self.layer_order:
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memory[layer_index] = self.layers[layer_index](memory[self.indices[layer_index]])
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outs.append(memory[self.num_input_nodes: self.num_input_nodes + self.action_num * 2])
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outs = torch.stack(outs)
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self.memory = torch.Tensor(memory)
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return torch.reshape(outs, (x.shape[0], 4, 2))
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else:
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memory[:, 0:self.num_input_nodes] = x
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for layer_index in self.layer_order:
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memory[:, layer_index] = self.layers[layer_index](memory[:, self.indices[layer_index]])
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return torch.reshape(
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memory[:, self.num_input_nodes: self.num_input_nodes + self.action_num * 2],
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(x.shape[0], 4, 2))
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if __name__ == '__main__':
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sample = np.random.random((1, 486))
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model = EvolutionModel(5, 4, 4).to(device)
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print(model)
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print(model.has_recurrent)
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test = model(torch.tensor(sample, dtype=torch.float32))
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# test = test.cpu().detach().numpy()
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print(test)
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state = np.random.random((1, 486))
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target = np.random.random((4, 2))
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states = [
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[state],
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[state],
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[state],
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[state],
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]
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targets = [
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[target],
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[target],
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[target],
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[target],
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]
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optimizer = torch.optim.RMSprop(model.parameters(), lr=1e-3)
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from labirinth_ai.Models.BaseModel import train
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train(states, targets, model, optimizer)
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@ -382,6 +382,8 @@ class NetLearner(Subject):
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self.lastRewards = []
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self.accumulated_rewards = 0
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def visualize(self):
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print(self.name)
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layers = self.model.get_weights()
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@ -542,6 +544,8 @@ class NetLearner(Subject):
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self.train()
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self.nextTrain = min(self.batchsize + self.nextTrain, (self.historySizeMul + 1) * self.batchsize)
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self.accumulated_rewards += self.lastReward
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self.lastAction = self.action
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self.lastState = self.state
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self.lastReward = 0
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@ -728,10 +732,12 @@ class Herbivore(NetLearner):
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if len(action) == 2:
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if len(world.subjectDict[(self.x + action[0], self.y + action[1])]) > 0:
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for sub in world.subjectDict[(self.x + action[0], self.y + action[1])]:
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if isinstance(sub, Hunter):
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if sub.alive:
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self.kills += 1
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sub.alive = False
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self.alive = True
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sub.kills += 1
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sub.alive = True
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sub.lastReward += 10
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self.alive = False
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self.lastRewards = []
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if right in directions:
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@ -795,6 +801,10 @@ class Herbivore(NetLearner):
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return action
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def respawnUpdate(self, x, y, world: LabyrinthWorld):
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super(Herbivore, self).respawnUpdate(x, y, world)
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self.lastReward -= 1
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class Hunter(NetLearner):
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name = 'Hunter'
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