deleted policy daptiom, added IAC
This commit is contained in:
Robert Müller
@ -1,100 +1,133 @@
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from environments.factory import make
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from salina import Workspace, TAgent
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from salina.agents.gyma import AutoResetGymAgent, GymAgent
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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
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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.nn.utils import spectral_norm
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import torch.optim as optim
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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 add_env_props, load_yaml_file, CombineActionsAgent
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class A2CAgent(TAgent):
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def __init__(self, observation_size, hidden_size, n_actions):
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def __init__(self, observation_size, hidden_size, n_actions, agent_id=-1, marl=False):
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super().__init__()
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observation_size = np.prod(observation_size)
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self.agent_id = agent_id
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self.marl = marl
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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, n_actions),
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)
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self.critic_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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spectral_norm(nn.Linear(hidden_size, 1)),
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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(("env/env_obs", t))
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if self.marl:
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observation = observation.permute(2, 0, 1, 3, 4, 5)
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observation = observation[self.agent_id]
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return observation
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def forward(self, t, stochastic, **kwargs):
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observation = self.get(("env/env_obs", t))
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scores = self.model(observation)
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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_model(observation).squeeze(-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(("action", t), action)
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self.set(("action_probs", t), probs)
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self.set(("critic", t), critic)
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agent_str = f'agent{self.agent_id}_'
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self.set((f'{agent_str}action', t), action)
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self.set((f'{agent_str}action_probs', t), probs)
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self.set((f'{agent_str}critic', t), critic)
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if __name__ == '__main__':
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# Setup agents and workspace
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env_agent = AutoResetGymAgent(make, dict(env_str='DirtyFactory-v0'), n_envs=1)
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a2c_agent = A2CAgent(3*4*5*5, 96, 10)
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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 = 1
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eval_agent = Agents(GymAgent(make, dict(env_str='DirtyFactory-v0'), n_envs=1), a2c_agent)
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for i in range(100):
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eval_agent(workspace, t=i, save_render=True, stochastic=True)
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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 = AutoResetGymAgent(
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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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'marl': n_agents > 1})
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for agent_id in range(n_agents)]
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assert False
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# combine agents
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acquisition_agent = TemporalAgent(Agents(env_agent, a2c_agent))
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acquisition_agent.seed(0)
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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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optimizer = optim.Adam(a2c_agent.parameters(), lr=1e-3)
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n_timesteps = 10
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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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for epoch in range(200000):
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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 k in workspace.keys():
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# print(f'{k} ==> {workspace[k].size()}')
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critic, done, action_probs, reward, action = workspace[
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"critic", "env/done", "action_probs", "env/reward", "action"
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]
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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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target = reward[1:] + 0.99 * critic[1:].detach() * (1 - done[1:].float())
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td = target - critic[:-1]
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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.zero_grad()
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loss.backward()
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optimizer.step()
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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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f"agent{agent_id}_critic", "env/done",
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f'agent{agent_id}_action_probs', "env/reward",
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f"agent{agent_id}_action"
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]
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td = gae(critic, reward, done, 0.99, 0.3)
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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(), 2)
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optimizer.step()
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# Compute the cumulated reward on final_state
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creward = workspace["env/cumulated_reward"]
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creward = creward[done]
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if creward.size()[0] > 0:
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cum_r = creward.mean().item()
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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(f"Cum. r: {cum_r:.2f}, Best r. so far: {best:.2f}", refresh=True)
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# Compute the cumulated reward on final_state
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creward = workspace["env/cumulated_reward"]
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creward = creward[done]
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if creward.size()[0] > 0:
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print(f"Cumulative reward at A2C step #{(1+epoch)*n_timesteps}: {creward.mean().item()}")
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