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ppo.py
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ppo.py
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import argparse
import os
import sys
import gym
from gym import wrappers
import random
import numpy as np
import torch
import torch.optim as optim
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from model import Model, Shared_obs_stats
class Params():
def __init__(self):
self.batch_size = 64
self.lr = 3e-4
self.gamma = 0.99
self.gae_param = 0.95
self.clip = 0.2
self.ent_coeff = 0.
self.num_epoch = 10
self.num_steps = 2048
self.time_horizon = 1000000
self.max_episode_length = 10000
self.seed = 1
#self.env_name = 'InvertedPendulum-v1'
self.env_name = 'InvertedDoublePendulum-v1'
#self.env_name = 'Reacher-v1'
#self.env_name = 'Pendulum-v0'
#self.env_name = 'Hopper-v1'
#self.env_name = 'Ant-v1'
#self.env_name = 'HalfCheetah-v1'
class ReplayMemory(object):
def __init__(self, capacity):
self.capacity = capacity
self.memory = []
def push(self, events):
for event in zip(*events):
self.memory.append(event)
if len(self.memory)>self.capacity:
del self.memory[0]
def clear(self):
self.memory = []
def sample(self, batch_size):
samples = zip(*random.sample(self.memory, batch_size))
return map(lambda x: torch.cat(x, 0), samples)
def normal(x, mu, sigma_sq):
a = (-1*(x-mu).pow(2)/(2*sigma_sq)).exp()
b = 1/(2*sigma_sq*np.pi).sqrt()
return a*b
def train(env, model, optimizer, shared_obs_stats):
memory = ReplayMemory(params.num_steps)
num_inputs = env.observation_space.shape[0]
num_outputs = env.action_space.shape[0]
state = env.reset()
state = Variable(torch.Tensor(state).unsqueeze(0))
done = True
# horizon loop
episode = -1
for t in range(params.time_horizon):
episode_length = 0
while(len(memory.memory)<params.num_steps):
states = []
actions = []
rewards = []
values = []
returns = []
advantages = []
av_reward = 0
cum_reward = 0
cum_done = 0
# n steps loops
for step in range(params.num_steps):
episode_length += 1
shared_obs_stats.observes(state)
state = shared_obs_stats.normalize(state)
states.append(state)
mu, sigma_sq, v = model(state)
eps = torch.randn(mu.size())
action = (mu + sigma_sq.sqrt()*Variable(eps))
actions.append(action)
values.append(v)
env_action = action.data.squeeze().numpy()
state, reward, done, _ = env.step(env_action)
done = (done or episode_length >= params.max_episode_length)
cum_reward += reward
reward = max(min(reward, 1), -1)
rewards.append(reward)
if done:
episode += 1
cum_done += 1
av_reward += cum_reward
cum_reward = 0
episode_length = 0
state = env.reset()
state = Variable(torch.Tensor(state).unsqueeze(0))
if done:
break
# one last step
R = torch.zeros(1, 1)
if not done:
_,_,v = model(state)
R = v.data
# compute returns and GAE(lambda) advantages:
R = Variable(R)
values.append(R)
A = Variable(torch.zeros(1, 1))
for i in reversed(range(len(rewards))):
td = rewards[i] + params.gamma*values[i+1].data[0,0] - values[i].data[0,0]
A = float(td) + params.gamma*params.gae_param*A
advantages.insert(0, A)
R = A + values[i]
returns.insert(0, R)
# store usefull info:
memory.push([states, actions, returns, advantages])
# epochs
model_old = Model(num_inputs, num_outputs)
model_old.load_state_dict(model.state_dict())
av_loss = 0
for k in range(params.num_epoch):
# cf https://github.com/openai/baselines/blob/master/baselines/pposgd/pposgd_simple.py
batch_states, batch_actions, batch_returns, batch_advantages = memory.sample(params.batch_size)
# old probas
mu_old, sigma_sq_old, v_pred_old = model_old(batch_states.detach())
probs_old = normal(batch_actions, mu_old, sigma_sq_old)
# new probas
mu, sigma_sq, v_pred = model(batch_states)
probs = normal(batch_actions, mu, sigma_sq)
# ratio
ratio = probs/(1e-15+probs_old)
# clip loss
surr1 = ratio * torch.cat([batch_advantages]*num_outputs,1) # surrogate from conservative policy iteration
surr2 = ratio.clamp(1-params.clip, 1+params.clip) * torch.cat([batch_advantages]*num_outputs,1)
loss_clip = -torch.mean(torch.min(surr1, surr2))
# value loss
vfloss1 = (v_pred - batch_returns)**2
v_pred_clipped = v_pred_old + (v_pred - v_pred_old).clamp(-params.clip, params.clip)
vfloss2 = (v_pred_clipped - batch_returns)**2
loss_value = 0.5*torch.mean(torch.max(vfloss1, vfloss2)) # also clip value loss
# entropy
loss_ent = -params.ent_coeff*torch.mean(probs*torch.log(probs+1e-5))
# total
total_loss = (loss_clip + loss_value + loss_ent)
av_loss += total_loss.data[0]/float(params.num_epoch)
# before Adam step, update old_model:
''' not sure about this '''
model_old.load_state_dict(model.state_dict())
# step
optimizer.zero_grad()
#model.zero_grad()
total_loss.backward(retain_variables=True)
optimizer.step()
# finish, print:
print('episode',episode,'av_reward',av_reward/float(cum_done),'av_loss',av_loss)
memory.clear()
def mkdir(base, name):
path = os.path.join(base, name)
if not os.path.exists(path):
os.makedirs(path)
return path
if __name__ == '__main__':
params = Params()
torch.manual_seed(params.seed)
work_dir = mkdir('exp', 'ppo')
monitor_dir = mkdir(work_dir, 'monitor')
env = gym.make(params.env_name)
#env = wrappers.Monitor(env, monitor_dir, force=True)
num_inputs = env.observation_space.shape[0]
num_outputs = env.action_space.shape[0]
model = Model(num_inputs, num_outputs)
shared_obs_stats = Shared_obs_stats(num_inputs)
optimizer = optim.Adam(model.parameters(), lr=params.lr)
train(env, model, optimizer, shared_obs_stats)