ppo_class_z.py

# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppopy

import argparse
import os
import random
import time
from distutils.util import strtobool

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions.categorical import Categorical
from torch.utils.tensorboard import SummaryWriter

from doorsenvs import DoorZ

from agents import PPOAgentwithZ


def parse_args():
    # fmt: off
    parser = argparse.ArgumentParser()
    parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
        help="the name of this experiment")
    parser.add_argument("--seed", type=int, default=1,
        help="seed of the experiment")
    parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
        help="if toggled, `torch.backends.cudnn.deterministic=False`")
    parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
        help="if toggled, cuda will be enabled by default")

    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
        help="whether to capture videos of the agent performances (check out `videos` folder)")

    # Algorithm specific arguments
    parser.add_argument("--env-id", type=str, default="DoorEnv_AIRL",
        help="the id of the environment")
    parser.add_argument("--total-timesteps", type=int, default=1000000,
        help="total timesteps of the experiments")
    parser.add_argument("--learning-rate", type=float, default=2.5e-4,
        help="the learning rate of the optimizer")
    parser.add_argument("--num-envs", type=int, default=4,
        help="the number of parallel game environments")
    parser.add_argument("--num-steps", type=int, default=32,
        help="the number of steps to run in each environment per policy rollout")
    parser.add_argument("--anneal-lr", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
        help="Toggle learning rate annealing for policy and value networks")
    parser.add_argument("--gamma", type=float, default=0.99,
        help="the discount factor gamma")
    parser.add_argument("--gae-lambda", type=float, default=0.95,
        help="the lambda for the general advantage estimation")
    parser.add_argument("--num-minibatches", type=int, default=4,
        help="the number of mini-batches")
    parser.add_argument("--update-epochs", type=int, default=4,
        help="the K epochs to update the policy")
    parser.add_argument("--norm-adv", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
        help="Toggles advantages normalization")
    parser.add_argument("--clip-coef", type=float, default=0.2,
        help="the surrogate clipping coefficient")
    parser.add_argument("--clip-vloss", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
        help="Toggles whether or not to use a clipped loss for the value function, as per the paper.")
    parser.add_argument("--ent-coef", type=float, default=0.05,
        help="coefficient of the entropy")
    parser.add_argument("--vf-coef", type=float, default=0.5,
        help="coefficient of the value function")
    parser.add_argument("--max-grad-norm", type=float, default=0.5,
        help="the maximum norm for the gradient clipping")
    parser.add_argument("--target-kl", type=float, default=None,
        help="the target KL divergence threshold")
    args = parser.parse_args()
    args.batch_size = int(args.num_envs * args.num_steps)
    args.minibatch_size = int(args.batch_size // args.num_minibatches)
    # fmt: on
    return args




def step_all(envs,actions):

    states,rewards,dones,infos = [],[],[],[]

    for i,env in enumerate(envs):
        state,reward,done,info = env.step(actions[i])
        states.append(state)
        rewards.append(reward)
        dones.append(done)
        infos.append(info)
    
    return states,rewards,dones,infos





class ppo_trainer_z():

    def __init__(self,agent=None,lr=2.5e-4,total_timesteps = 1000000,buffer_size = 3000,env_id = ""):

        self.args = parse_args()
        run_name = f"{env_id}__{self.args.exp_name}__{self.args.seed}__{int(time.time())}"

        self.writer = SummaryWriter(f"runs/{run_name}")
        self.writer.add_text(
            "hyperparameters",
            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(self.args).items()])),
        )

        # TRY NOT TO MODIFY: seeding
        random.seed(self.args.seed)
        np.random.seed(self.args.seed)
        torch.manual_seed(self.args.seed)
        torch.backends.cudnn.deterministic = self.args.torch_deterministic

        self.device = torch.device("cuda" if torch.cuda.is_available() and self.args.cuda else "cpu")

        if agent:
            self.agent = agent.to(self.device)
        else:
            self.agent = PPOAgentwithZ(self.envs).to(self.device)

        self.optimizer = optim.Adam(self.agent.parameters(), lr=lr, eps=1e-5)
        self.buffersize = buffer_size
        self.envs = [DoorZ(max_steps=self.args.num_steps,seed=self.args.seed + i) for i in range(self.args.num_envs)]
        self.num_updates = total_timesteps // self.args.batch_size

        self.reset()

    def reset(self):
        # env setup
        #envs = [make_env(args.seed + i) for i in range(args.num_envs)]

        # ALGO Logic: Storage setup
        self.obs = torch.zeros((self.args.num_steps, self.args.num_envs) + self.envs[0].single_observation_space).to(self.device)
        self.actions = torch.zeros((self.args.num_steps, self.args.num_envs) + self.envs[0].single_action_space).to(self.device)
        self.Z = torch.zeros((self.args.num_steps, self.args.num_envs,self.envs[0].classes)).to(self.device)

        self.logprobs = torch.zeros((self.args.num_steps, self.args.num_envs)).to(self.device)
        self.rewards = torch.zeros((self.args.num_steps, self.args.num_envs)).to(self.device)
        self.dones = torch.zeros((self.args.num_steps, self.args.num_envs)).to(self.device)
        self.values = torch.zeros((self.args.num_steps, self.args.num_envs)).to(self.device)


        # TRY NOT TO MODIFY: start the game
        self.global_step = 0
        self.start_time = time.time()
  

        self.all_obs = self.obs.reshape((-1,) + self.envs[0].single_observation_space)[:1,...]
        self.all_acts = self.actions.reshape((-1,) + self.envs[0].single_action_space)[:1,...]
        self.all_Zs = self.Z.reshape((-1, self.envs[0].classes))[:1,...]
        self.all_logprobs = self.logprobs.reshape((-1,1))[:1,...]

    def train(self):

        next_obs = torch.Tensor([env.reset() for env in self.envs]).to(self.device)
        next_z = torch.vstack([env.gt_z for env in self.envs]).to(self.device)
        next_done = torch.zeros(self.args.num_envs).to(self.device)

        for update in range(1, self.num_updates + 1):
            # Annealing the rate if instructed to do so.
            if self.args.anneal_lr:
                frac = 1.0 - (update - 1.0) / self.num_updates
                lrnow = frac * self.args.learning_rate
                self.optimizer.param_groups[0]["lr"] = lrnow

            for step in range(0, self.args.num_steps):
                self.global_step += 1 * self.args.num_envs
                self.obs[step] = next_obs
                self.Z[step] = next_z
                self.dones[step] = next_done

                # ALGO LOGIC: action logic
                with torch.no_grad():
                    action, logprob, _, value = self.agent.get_action_and_value(next_obs,z=next_z)
                    self.values[step] = value.flatten()
                self.actions[step] = torch.functional.F.one_hot(action,num_classes=self.envs[0].single_action_space[0])
                self.logprobs[step] = logprob

                # TRY NOT TO MODIFY: execute the game and log data.
                #next_obs, reward, done, info = envs.step(action.cpu().numpy())
                next_obs, reward, done, info = step_all(self.envs,action.cpu().numpy())
                next_z = torch.vstack([env.gt_z for env in self.envs]).to(self.device)
                self.rewards[step] = torch.tensor(reward).to(self.device).view(-1)
                next_obs, next_done = torch.Tensor(next_obs).to(self.device), torch.Tensor(done).to(self.device)

                for item in info:
                    if "episode" in item.keys():
                        print(f"global_step={self.global_step}, episodic_return={item['episode']['r']}")
                        self.writer.add_scalar("charts/episodic_return", item["episode"]["r"], self.global_step)
                        self.writer.add_scalar("charts/episodic_length", item["episode"]["l"], self.global_step)
                        break

            # bootstrap value if not done
            with torch.no_grad():
                next_value = self.agent.get_value(next_obs).reshape(1, -1)
                advantages = torch.zeros_like(self.rewards).to(self.device)
                lastgaelam = 0
                for t in reversed(range(self.args.num_steps)):
                    if t == self.args.num_steps - 1:
                        nextnonterminal = 1.0 - next_done
                        nextvalues = next_value
                    else:
                        nextnonterminal = 1.0 - self.dones[t + 1]
                        nextvalues = self.values[t + 1]
                    delta = self.rewards[t] + self.args.gamma * nextvalues * nextnonterminal - self.values[t]
                    advantages[t] = lastgaelam = delta + self.args.gamma * self.args.gae_lambda * nextnonterminal * lastgaelam
                returns = advantages + self.values

            # flatten the batch
            b_obs = self.obs.reshape((-1,) + self.envs[0].single_observation_space)
            b_logprobs = self.logprobs.reshape(-1)
            b_actions = self.actions.reshape((-1,) + self.envs[0].single_action_space)
            b_Z = self.Z.reshape((-1, self.envs[0].classes))
            b_advantages = advantages.reshape(-1)
            b_returns = returns.reshape(-1)
            b_values = self.values.reshape(-1)

            self.all_obs = torch.vstack((self.all_obs,b_obs))
            self.all_acts = torch.vstack((self.all_acts,b_actions))
            self.all_Zs = torch.vstack((self.all_Zs,b_Z))
            self.all_logprobs = torch.vstack((self.all_logprobs,b_logprobs[:,None]))

            # Optimizing the policy and value network
            b_inds = np.arange(self.args.batch_size)
            clipfracs = []
            for epoch in range(self.args.update_epochs):
                np.random.shuffle(b_inds)
                for start in range(0, self.args.batch_size, self.args.minibatch_size):
                    end = start + self.args.minibatch_size
                    mb_inds = b_inds[start:end]
                    _, newlogprob, entropy, newvalue = self.agent.get_action_and_value(b_obs[mb_inds], b_actions.long()[mb_inds].argmax(dim=1),z=b_Z[mb_inds])
                    logratio = newlogprob - b_logprobs[mb_inds]
                    ratio = logratio.exp()

                    with torch.no_grad():
                        # calculate approx_kl http://joschu.net/blog/kl-approx.html
                        old_approx_kl = (-logratio).mean()
                        approx_kl = ((ratio - 1) - logratio).mean()
                        clipfracs += [((ratio - 1.0).abs() > self.args.clip_coef).float().mean().item()]

                    mb_advantages = b_advantages[mb_inds]
                    if self.args.norm_adv:
                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)

                    # Policy loss
                    pg_loss1 = -mb_advantages * ratio
                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - self.args.clip_coef, 1 + self.args.clip_coef)
                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()

                    # Value loss
                    newvalue = newvalue.view(-1)
                    if self.args.clip_vloss:
                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
                        v_clipped = b_values[mb_inds] + torch.clamp(
                            newvalue - b_values[mb_inds],
                            -self.args.clip_coef,
                            self.args.clip_coef,
                        )
                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
                        v_loss = 0.5 * v_loss_max.mean()
                    else:
                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()

                    entropy_loss = entropy.mean()
                    loss = pg_loss - self.args.ent_coef * entropy_loss + v_loss * self.args.vf_coef

                    self.optimizer.zero_grad()
                    loss.backward()
                    nn.utils.clip_grad_norm_(self.agent.parameters(), self.args.max_grad_norm)
                    self.optimizer.step()

                if self.args.target_kl is not None:
                    if approx_kl > self.args.target_kl:
                        break

            y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
            var_y = np.var(y_true)
            explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y

            # TRY NOT TO MODIFY: record rewards for plotting purposes
            self.writer.add_scalar("charts/learning_rate", self.optimizer.param_groups[0]["lr"], self.global_step)
            self.writer.add_scalar("losses/value_loss", v_loss.item(), self.global_step)
            self.writer.add_scalar("losses/policy_loss", pg_loss.item(), self.global_step)
            self.writer.add_scalar("losses/entropy", entropy_loss.item(), self.global_step)
            self.writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), self.global_step)
            self.writer.add_scalar("losses/approx_kl", approx_kl.item(), self.global_step)
            self.writer.add_scalar("losses/clipfrac", np.mean(clipfracs), self.global_step)
            self.writer.add_scalar("losses/explained_variance", explained_var, self.global_step)
            print("SPS:", int(self.global_step / (time.time() - self.start_time)))
            self.writer.add_scalar("charts/SPS", int(self.global_step / (time.time() - self.start_time)), self.global_step)

        self.all_acts = self.all_acts[-self.buffersize:]
        self.all_Zs = self.all_Zs[-self.buffersize:]
        self.all_obs = self.all_obs[-self.buffersize:]
        self.all_logprobs = self.all_logprobs[-self.buffersize:]

        return self.all_obs,self.all_acts,self.all_logprobs,self.all_Zs

    def close(self):

        self.writer.close()

    #torch.save(agent,f'ppo_agent_iter_{self.args.total_timesteps}_mlp.pth')