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https://github.com/illiumst/marl-factory-grid.git
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Merge remote-tracking branch 'origin/main'
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Steffen Illium
23
studies/normalization_study.py
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23
studies/normalization_study.py
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from algorithms.utils import Checkpointer
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from pathlib import Path
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from algorithms.utils import load_yaml_file, add_env_props, instantiate_class, load_class
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#from algorithms.marl import LoopSNAC, LoopIAC, LoopSEAC
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for i in range(0, 5):
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for name in ['snac', 'mappo', 'iac', 'seac']:
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study_root = Path(__file__).parent / name
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cfg = load_yaml_file(study_root / f'{name}.yaml')
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add_env_props(cfg)
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env = instantiate_class(cfg['env'])
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net = instantiate_class(cfg['agent'])
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max_steps = cfg['algorithm']['max_steps']
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n_steps = cfg['algorithm']['n_steps']
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checkpointer = Checkpointer(f'{name}#{i}', study_root, cfg, max_steps, 50)
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loop = load_class(cfg['method'])(cfg)
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df = loop.train_loop(checkpointer)
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22
studies/playground_file.py
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studies/playground_file.py
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import pandas as pd
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from pathlib import Path
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import matplotlib.pyplot as plt
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import seaborn as sns
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dfs = []
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for name in ['mappo']:
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for c in range(5):
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try:
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study_root = Path(__file__).parent / name / f'{name}#{c}'
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print(study_root)
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df = pd.read_csv(study_root / 'results.csv', index_col=False)
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df.reward = df.reward.rolling(100).mean()
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df['method'] = name.upper()
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dfs.append(df)
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except Exception as e:
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pass
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df = pd.concat(dfs).reset_index()
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sns.lineplot(data=df, x='steps', y='reward', hue='method', palette='husl', ci='sd', linewidth=1.5, err_style='bars')
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plt.savefig('study.png')
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print('saved image')
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@ -1,139 +0,0 @@
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from salina.agents.gyma import AutoResetGymAgent
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from salina.agents import Agents, TemporalAgent
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from salina.rl.functional import _index, gae
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import torch
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import torch.nn as nn
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from torch.distributions import Categorical
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from salina import TAgent, Workspace, get_arguments, get_class, instantiate_class
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from pathlib import Path
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import numpy as np
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from tqdm import tqdm
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import time
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from algorithms.utils import (
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add_env_props,
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load_yaml_file,
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CombineActionsAgent,
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AutoResetGymMultiAgent,
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access_str,
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AGENT_PREFIX, REWARD, CUMU_REWARD, OBS, SEP
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)
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class A2CAgent(TAgent):
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def __init__(self, observation_size, hidden_size, n_actions, agent_id):
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super().__init__()
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observation_size = np.prod(observation_size)
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print(observation_size)
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self.agent_id = agent_id
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self.model = nn.Sequential(
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nn.Flatten(),
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nn.Linear(observation_size, hidden_size),
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nn.ELU(),
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nn.Linear(hidden_size, hidden_size),
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nn.ELU(),
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nn.Linear(hidden_size, hidden_size),
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nn.ELU()
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)
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self.action_head = nn.Linear(hidden_size, n_actions)
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self.critic_head = nn.Linear(hidden_size, 1)
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def get_obs(self, t):
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observation = self.get((f'env/{access_str(self.agent_id, OBS)}', t))
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return observation
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def forward(self, t, stochastic, **kwargs):
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observation = self.get_obs(t)
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features = self.model(observation)
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scores = self.action_head(features)
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probs = torch.softmax(scores, dim=-1)
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critic = self.critic_head(features).squeeze(-1)
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if stochastic:
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action = torch.distributions.Categorical(probs).sample()
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else:
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action = probs.argmax(1)
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self.set((f'{access_str(self.agent_id, "action")}', t), action)
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self.set((f'{access_str(self.agent_id, "action_probs")}', t), probs)
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self.set((f'{access_str(self.agent_id, "critic")}', t), critic)
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if __name__ == '__main__':
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# Setup workspace
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uid = time.time()
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workspace = Workspace()
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n_agents = 2
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# load config
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cfg = load_yaml_file(Path(__file__).parent / 'sat_mad.yaml')
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add_env_props(cfg)
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cfg['env'].update({'n_agents': n_agents})
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# instantiate agent and env
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env_agent = AutoResetGymMultiAgent(
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get_class(cfg['env']),
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get_arguments(cfg['env']),
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n_envs=1
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)
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a2c_agents = [instantiate_class({**cfg['agent'],
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'agent_id': agent_id})
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for agent_id in range(n_agents)]
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# combine agents
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acquisition_agent = TemporalAgent(Agents(env_agent, *a2c_agents, CombineActionsAgent()))
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acquisition_agent.seed(69)
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# optimizers & other parameters
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cfg_optim = cfg['algorithm']['optimizer']
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optimizers = [get_class(cfg_optim)(a2c_agent.parameters(), **get_arguments(cfg_optim))
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for a2c_agent in a2c_agents]
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n_timesteps = cfg['algorithm']['n_timesteps']
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# Decision making loop
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best = -float('inf')
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with tqdm(range(int(cfg['algorithm']['max_epochs'] / n_timesteps))) as pbar:
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for epoch in pbar:
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workspace.zero_grad()
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if epoch > 0:
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workspace.copy_n_last_steps(1)
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acquisition_agent(workspace, t=1, n_steps=n_timesteps-1, stochastic=True)
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else:
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acquisition_agent(workspace, t=0, n_steps=n_timesteps, stochastic=True)
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for agent_id in range(n_agents):
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critic, done, action_probs, reward, action = workspace[
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access_str(agent_id, 'critic'),
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"env/done",
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access_str(agent_id, 'action_probs'),
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access_str(agent_id, 'reward', 'env/'),
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access_str(agent_id, 'action')
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]
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td = gae(critic, reward, done, 0.98, 0.25)
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td_error = td ** 2
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critic_loss = td_error.mean()
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entropy_loss = Categorical(action_probs).entropy().mean()
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action_logp = _index(action_probs, action).log()
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a2c_loss = action_logp[:-1] * td.detach()
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a2c_loss = a2c_loss.mean()
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loss = (
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-0.001 * entropy_loss
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+ 1.0 * critic_loss
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- 0.1 * a2c_loss
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)
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optimizer = optimizers[agent_id]
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optimizer.zero_grad()
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loss.backward()
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#torch.nn.utils.clip_grad_norm_(a2c_agents[agent_id].parameters(), .5)
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optimizer.step()
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# Compute the cumulated reward on final_state
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rews = ''
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for agent_i in range(n_agents):
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creward = workspace['env/'+access_str(agent_i, CUMU_REWARD)]
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creward = creward[done]
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if creward.size()[0] > 0:
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rews += f'{AGENT_PREFIX}{agent_i}: {creward.mean().item():.2f} | '
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"""if cum_r > best:
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torch.save(a2c_agent.state_dict(), Path(__file__).parent / f'agent_{uid}.pt')
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best = cum_r"""
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pbar.set_description(rews, refresh=True)
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@ -1,27 +0,0 @@
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agent:
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classname: studies.sat_mad.A2CAgent
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observation_size: 4*5*5
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hidden_size: 128
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n_actions: 10
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env:
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classname: environments.factory.make
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env_name: "DirtyFactory-v0"
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n_agents: 1
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pomdp_r: 2
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max_steps: 400
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stack_n_frames: 3
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individual_rewards: True
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algorithm:
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max_epochs: 1000000
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n_envs: 1
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n_timesteps: 10
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discount_factor: 0.99
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entropy_coef: 0.01
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critic_coef: 1.0
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gae: 0.25
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optimizer:
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classname: torch.optim.Adam
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lr: 0.0003
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weight_decay: 0.0
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36
studies/viz_policy.py
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36
studies/viz_policy.py
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import pandas as pd
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from algorithms.marl import LoopSNAC, LoopIAC, LoopSEAC
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from pathlib import Path
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from algorithms.utils import load_yaml_file
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from tqdm import trange
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study = 'example_config#0'
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#study_root = Path(__file__).parent / study
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study_root = Path('/Users/romue/PycharmProjects/EDYS/algorithms/marl/')
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#['L2NoAh_gru', 'L2NoCh_gru', 'nomix_gru']:
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render = True
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eval_eps = 3
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for run in range(0, 5):
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for name in ['example_config']:#['L2OnlyAh_gru', 'L2OnlyChAh_gru', 'L2OnlyMix_gru']: #['layernorm_gru', 'basic_gru', 'nonorm_gru', 'spectralnorm_gru']:
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cfg = load_yaml_file(study_root / study / 'config.yaml')
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#p_root = Path(study_root / study / f'{name}#{run}')
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dfs = []
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for i in trange(500):
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path = study_root / study / f'checkpoint_{161}'
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print(path)
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snac = LoopSEAC(cfg)
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snac.load_state_dict(path)
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snac.eval()
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df = snac.eval_loop(render=render, n_episodes=eval_eps)
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df['checkpoint'] = i
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dfs.append(df)
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results = pd.concat(dfs)
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results['run'] = run
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results.to_csv(p_root / 'results.csv', index=False)
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#sns.lineplot(data=results, x='checkpoint', y='reward', hue='agent', palette='husl')
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#plt.savefig(f'{experiment_name}.png')
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@ -1,39 +0,0 @@
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from salina.agents import Agents, TemporalAgent
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import torch
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from salina import Workspace, get_arguments, get_class, instantiate_class
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from pathlib import Path
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from salina.agents.gyma import GymAgent
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import time
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from algorithms.utils import load_yaml_file, add_env_props
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if __name__ == '__main__':
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# Setup workspace
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uid = time.time()
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workspace = Workspace()
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weights = Path('/Users/romue/PycharmProjects/EDYS/studies/agent_1636994369.145843.pt')
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cfg = load_yaml_file(Path(__file__).parent / 'sat_mad.yaml')
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add_env_props(cfg)
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cfg['env'].update({'n_agents': 2})
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# instantiate agent and env
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env_agent = GymAgent(
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get_class(cfg['env']),
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get_arguments(cfg['env']),
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n_envs=1
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)
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agents = []
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for _ in range(2):
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a2c_agent = instantiate_class(cfg['agent'])
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if weights:
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a2c_agent.load_state_dict(torch.load(weights))
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agents.append(a2c_agent)
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# combine agents
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acquisition_agent = TemporalAgent(Agents(env_agent, *agents))
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acquisition_agent.seed(42)
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acquisition_agent(workspace, t=0, n_steps=400, stochastic=False, save_render=True)
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