def main():
seeding()
# number of parallel agents
parallel_envs = 4
# number of training episodes.
# change this to higher number to experiment. say 30000.
number_of_episodes = 10000
episode_length = 100
batchsize = 1000
# how many episodes to save policy and gif
save_interval = 5000
# what is this ?
t = 0
# amplitude of OU noise
# this slowly decreases to 0
noise = 2
noise_reduction = 0.9999
# how many episodes before update
episode_per_update = 2 * parallel_envs
log_path = os.getcwd() + "/log"
model_dir = os.getcwd() + "/model_dir"
os.makedirs(model_dir, exist_ok=True)
torch.set_num_threads(parallel_envs)
# this may be a list of all environments
env = envs.make_parallel_env(parallel_envs)
# keep 5000 episodes worth of replay
buffer = ReplayBuffer(int(5000 * episode_length))
# initialize policy and critic
# this creates a list of models, each element in the list refers to an agent in the simulation
# [agent_one_ddpg, agent_two_ddpg, ...]
# agent_one_ddpg contains the agent actor and critic models,e.g., agent_one_ddpg.actor, agent_one_ddpg.critic
maddpg = MADDPG()
logger = SummaryWriter(log_dir=log_path)
agent0_reward = []
agent1_reward = []
agent2_reward = []
# training loop
# show progressbar
import progressbar as pb
widget = [
'episode: ',
pb.Counter(), '/',
str(number_of_episodes), ' ',
pb.Percentage(), ' ',
pb.ETA(), ' ',
pb.Bar(marker=pb.RotatingMarker()), ' '
]
timer = pb.ProgressBar(widgets=widget, maxval=number_of_episodes).start()
# use keep_awake to keep workspace from disconnecting
# for episode in keep_awake(range(0, number_of_episodes, parallel_envs)):
# notice we jump forward by number of parallel environments
for episode in range(0, number_of_episodes, parallel_envs):
timer.update(episode)
# i believe there are as many as number of agents times parallel env reward
reward_this_episode = np.zeros((parallel_envs, 3))
# obs is the observation state space of all the three agents in the 4 parallel env.
# for the Physical Dception environment with three agents it is of dimension 4x3x14.
# obs_full is world state irrespective of the agents and its dimension is 4x14.
# all_observation = array(number of environments 4, 2 elements)
# element 0 : is a list that contains 3 arrays. contains the state for each agent, each state is of size 14
# element 1 : global state from the perspective of the target/green for its environment. contains 14 elements
all_obs = env.reset()
# obs : is a list that has 1 element per environment. each element contains a list of 3 array.
# each array is the state of one agent in that environment.
# obs_full: is the god eye view of each environment. So it a list, that has 1 element per environment
# each element contains an array of 14 values which is the global state of that environment
obs, obs_full = transpose_list(all_obs)
#for calculating rewards for this particular episode - addition of all time steps
# save info or not
save_info = (episode % save_interval < parallel_envs
or episode == number_of_episodes - parallel_envs)
frames = []
tmax = 0
if save_info:
frames.append(env.render('rgb_array'))
for episode_t in range(episode_length):
# we finish the episode before sampling the buffer for trainint
# t jumps forward in a multiple of environment
t += parallel_envs
# explore = only explore for a certain number of episodes
# action input needs to be transposed
# the transpose_to_tensor(obs) changes the data to each agent point of view
# since we have 4 environments, there are 4 agent 1, 4 agent 2, and 4 agent 3
# each agent has a state in each environment, total states across 4 environments for agent 1 is 4x14 tensor
# transpose_to_tensor(obs) = is a list of 3 elements. each element is for 1 agent
# pick element 1. this is an array of 4x14 elements of agent observation across 4 environments.
# maddpg.act has a for loop that take each element of obs and pass it to the agents actor models and
# to generate an action from each agent actor.
actions = maddpg.act(transpose_to_tensor(obs), noise=noise)
noise *= noise_reduction
# there are 4 actions per agent and 3 agents, total of 12 . Each action has 2 elements force in x, y direct
# actions_array is a tensor of shape (3 agent, 4 env, 2 action)
actions_array = torch.stack(actions).detach().numpy()
# transpose the list of list
# flip the first two indices
# input to step requires the first index to correspond to number of parallel agents
# the shape of actions_for_env is (4 env, 3 agent, 2 action)
actions_for_env = np.rollaxis(actions_array, 1)
# step forward one frame
# obs is the observation state space of all the three agents in the 4 parallel env.
# for the Physical Dception environment with three agents it is of dimension 4x3x14.
# obs_full is world state irrespective of the agents and its dimension is 4x14.
# To gain more understanding, please see the code in the multiagent folder.
next_obs, next_obs_full, rewards, dones, info = env.step(
actions_for_env)
# add data to buffer
transition = (obs, obs_full, actions_for_env, rewards, next_obs,
next_obs_full, dones)
buffer.push(transition)
reward_this_episode += rewards
obs, obs_full = next_obs, next_obs_full
# save gif frame
if save_info:
frames.append(env.render('rgb_array'))
tmax += 1
# update once after every episode_per_update
if len(buffer
) > batchsize and episode % episode_per_update < parallel_envs:
for a_i in range(3):
# although samples are drawn randomly, for each sample we have all 3 agents data, and we know which
# reward and actions belong to which agent
# samples is a list of 7 elements: obs, obs_full, action, reward, next_obs, next_obs_full, done
# each element of sample, say samples[0] is a list of 3 elements, one for each agent
# each agent element contains their corresponding value, for example in case of obs it would be a
# vector with 14 values
# so when i ask for 2 samples for examples, i get 2 samples each containing all 3 agents states, rewards
samples = buffer.sample(batchsize)
maddpg.update(samples, a_i, logger)
maddpg.update_targets(
) #soft update the target network towards the actual networks
for i in range(parallel_envs):
agent0_reward.append(reward_this_episode[i, 0])
agent1_reward.append(reward_this_episode[i, 1])
agent2_reward.append(reward_this_episode[i, 2])
if episode % 100 == 0 or episode == number_of_episodes - 1:
avg_rewards = [
np.mean(agent0_reward),
np.mean(agent1_reward),
np.mean(agent2_reward)
]
agent0_reward = []
agent1_reward = []
agent2_reward = []
for a_i, avg_rew in enumerate(avg_rewards):
logger.add_scalar('agent%i/mean_episode_rewards' % a_i,
avg_rew, episode)
#saving model
save_dict_list = []
if save_info:
for i in range(3):
save_dict = {
'actor_params':
maddpg.maddpg_agent[i].actor.state_dict(),
'actor_optim_params':
maddpg.maddpg_agent[i].actor_optimizer.state_dict(),
'critic_params':
maddpg.maddpg_agent[i].critic.state_dict(),
'critic_optim_params':
maddpg.maddpg_agent[i].critic_optimizer.state_dict()
}
save_dict_list.append(save_dict)
torch.save(
save_dict_list,
os.path.join(model_dir, 'episode-{}.pt'.format(episode)))
# save gif files
imageio.mimsave(os.path.join(model_dir,
'episode-{}.gif'.format(episode)),
frames,
duration=.04)
env.close()
logger.close()
timer.finish()
def update(self, samples, agent_number, log):
"""update the critics and actors of all the agents """
obs, obs_full, action, reward, next_obs, next_obs_full, done = zip(
*samples)
#input needs to be transposed from [samples of (agent 1 obs, agent 2 obs)] to [agent 1 (sample obs), agent 2 (sample obs)]
agent1_next_obs, agent2_next_obs = zip(*next_obs)
agent1_obs, agent2_obs = zip(*obs)
obs = [
torch.from_numpy(np.vstack(agent1_obs)).float().to(device),
torch.from_numpy(np.vstack(agent2_obs)).float().to(device)
]
done = transpose_to_nested_list(done)
reward = transpose_to_nested_list(reward)
# always update agent 0, then copy the networks to the other agent(s)
agent = self.maddpg_agent[0]
agent.critic_optimizer.zero_grad()
#critic loss = batch mean of (y- Q(s,a) from target network)^2
#y = reward of this timestep + discount * Q(st+1,at+1) from target network
target_actions = self.target_act([agent1_next_obs, agent2_next_obs])
#stack the various agents actions and observations to input to the critic
target_actions = torch.cat(target_actions, 1)
next_obs_full = torch.from_numpy(
np.vstack(next_obs_full)).float().to(device)
target_critic_input = torch.cat((next_obs_full, target_actions),
1).to(device)
with torch.no_grad():
q_next = self.maddpg_agent[0].target_critic(target_critic_input)
y = torch.from_numpy(reward[agent_number]).float().to(
device) + self.discount_factor * q_next * (1 - torch.from_numpy(
done[agent_number].astype(np.uint8)).float().to(device))
action = transpose_to_tensor(action)
action = torch.cat(list(action), dim=1)
critic_input = torch.cat(
(torch.from_numpy(np.vstack(obs_full)).float().to(device), action),
1).to(device)
q = agent.critic(critic_input)
critic_loss = torch.nn.functional.mse_loss(q, y.detach())
critic_loss.backward()
torch.nn.utils.clip_grad_norm_(agent.critic.parameters(), 1.0)
agent.critic_optimizer.step()
#update actor network using policy gradient
agent.actor_optimizer.zero_grad()
# make input to agent
# detach the other agents to save computation
# saves some time for computing derivative
q_input = [ self.maddpg_agent[i].actor(ob) if i == agent_number \
else self.maddpg_agent[i].actor(ob).detach()
for i, ob in enumerate(obs) ]
q_input = torch.cat(q_input, dim=1)
obs_full = torch.from_numpy(np.vstack(obs_full)).float().to(device)
# combine all the actions and observations for input to critic
# many of the obs are redundant, and obs[1] contains all useful information already
q_input2 = torch.cat((obs_full, q_input), dim=1)
# get the policy gradient
actor_loss = -agent.critic(q_input2).mean()
actor_loss.backward()
torch.nn.utils.clip_grad_norm_(agent.actor.parameters(), 1.0)
agent.actor_optimizer.step()
self.copy_actor(0) # copy updated actor network to the other agents
al = actor_loss.cpu().detach().item()
cl = critic_loss.cpu().detach().item()
#log.info('agent{}: critic loss\t{}\tactor loss\t{}'.format(agent_number,cl,al))
return [al, cl]
def main():
seeding()
# number of parallel agents
parallel_envs = 4
# number of training episodes.
# change this to higher number to experiment. say 30000.
number_of_episodes = 1000
episode_length = 80
batchsize = 1000
# how many episodes to save policy and gif
save_interval = 1000
t = 0
# amplitude of OU noise
# this slowly decreases to 0
noise = 2
noise_reduction = 0.9999
# how many episodes before update
episode_per_update = 2 * parallel_envs
log_path = os.getcwd() + "/log"
model_dir = os.getcwd() + "/model_dir"
os.makedirs(model_dir, exist_ok=True)
torch.set_num_threads(parallel_envs)
env = envs.make_parallel_env(parallel_envs)
# keep 5000 episodes worth of replay
buffer = ReplayBuffer(int(5000 * episode_length))
# initialize policy and critic
maddpg = MADDPG()
logger = SummaryWriter(log_dir=log_path)
agent0_reward = []
agent1_reward = []
agent2_reward = []
# training loop
# show progressbar
widget = [
'episode: ',
pb.Counter(), '/',
str(number_of_episodes), ' ',
pb.Percentage(), ' ',
pb.ETA(), ' ',
pb.Bar(marker=pb.RotatingMarker()), ' '
]
timer = pb.ProgressBar(widgets=widget, maxval=number_of_episodes).start()
# use keep_awake to keep workspace from disconnecting
for episode in range(0, number_of_episodes + parallel_envs, parallel_envs):
timer.update(episode)
reward_this_episode = np.zeros((parallel_envs, 3))
all_obs = env.reset()
obs, obs_full = transpose_list(all_obs)
# for calculating rewards for this particular episode - addition of all time steps
# save info or not
save_info = (episode % save_interval < parallel_envs)
frames = []
tmax = 0
if save_info:
frames.append(env.render('rgb_array'))
for episode_t in range(episode_length):
t += parallel_envs
# explore = only explore for a certain number of episodes
# action input needs to be transposed
actions = maddpg.act(transpose_to_tensor(obs), noise=noise)
noise *= noise_reduction
actions_array = torch.stack(actions).detach().numpy()
# transpose the list of list
# flip the first two indices
# input to step requires the first index to correspond to number of parallel agents
actions_for_env = np.rollaxis(actions_array, 1)
# step forward one frame
next_obs, next_obs_full, rewards, dones, info = env.step(
actions_for_env)
# add data to buffer
transition = (obs, obs_full, actions_for_env, rewards, next_obs,
next_obs_full, dones)
buffer.push(transition)
reward_this_episode += rewards
obs, obs_full = next_obs, next_obs_full
# save gif frame
if save_info:
frames.append(env.render('rgb_array'))
tmax += 1
# update once after every episode_per_update
if len(buffer
) > batchsize and episode % episode_per_update < parallel_envs:
for a_i in range(3):
samples = buffer.sample(batchsize)
maddpg.update(samples, a_i, logger)
maddpg.update_targets(
) # soft update the target network towards the actual networks
for i in range(parallel_envs):
agent0_reward.append(reward_this_episode[i, 0])
agent1_reward.append(reward_this_episode[i, 1])
agent2_reward.append(reward_this_episode[i, 2])
if episode % 100 == 0 or episode == number_of_episodes - 1:
avg_rewards = [
np.mean(agent0_reward),
np.mean(agent1_reward),
np.mean(agent2_reward)
]
agent0_reward = []
agent1_reward = []
agent2_reward = []
for a_i, avg_rew in enumerate(avg_rewards):
logger.add_scalar('agent%i/mean_episode_rewards' % a_i,
avg_rew, episode)
# saving model
save_dict_list = []
if save_info:
for i in range(3):
save_dict = {
'actor_params':
maddpg.maddpg_agent[i].actor.state_dict(),
'actor_optim_params':
maddpg.maddpg_agent[i].actor_optimizer.state_dict(),
'critic_params':
maddpg.maddpg_agent[i].critic.state_dict(),
'critic_optim_params':
maddpg.maddpg_agent[i].critic_optimizer.state_dict()
}
save_dict_list.append(save_dict)
torch.save(
save_dict_list,
os.path.join(model_dir, 'episode-{}.pt'.format(episode)))
# save gif files
imageio.mimsave(os.path.join(model_dir,
'episode-{}.gif'.format(episode)),
frames,
duration=.04)
env.close()
logger.close()
timer.finish()
def update(self, samples, agent_number, logger):
"""update the critics and actors of all the agents"""
# `samples`: a list of batchsize, List(5,)
# `states` & next_states: a list of batchsize, Array(2,24)
# `actions`: a list of batchsize, Array(2,2)
# `rewards` & `dones`: a list of batch size, List(2,)
states, actions, rewards, next_states, dones = zip(*samples)
# -------------------------- preprocessing -------------------------- #
# `states` & `next_states`: a list of size 2, Tensor(batchsize,24)
states = transpose_to_tensor(states)
next_states = transpose_to_tensor(next_states)
# `states_full` & `next_states_full`: Tensor(batchsize,48)
states_full = torch.cat(states, dim=1)
next_states_full = torch.cat(next_states, dim=1)
# `actions`: Tensor(batchsize,4)
actions = transpose_to_tensor(actions)
actions = torch.cat(actions, dim=1)
# `dones` & `rewards`: a list of 2, Tensor(batchsize,)
dones = transpose_to_tensor(transpose_list(zip(*dones)))
rewards = transpose_to_tensor(transpose_list(zip(*rewards)))
# -------------------------- update critic -------------------------- #
agent = self.maddpg_agent[agent_number]
agent.critic_optimizer.zero_grad()
# critic loss = batch mean of (y - Q(s,a) from target network)^2
# y = current reward + discount * Q(st+1,at+1) from target network
target_actions = self.target_act(next_states)
target_actions = torch.cat(target_actions, dim=-1)
target_critic_input = torch.cat((next_states_full, target_actions),
dim=1).to(device)
with torch.no_grad():
q_next = agent.target_critic(target_critic_input)
y = rewards[agent_number].view(-1, 1) + \
self.discount_factor * q_next * \
(1 - dones[agent_number].view(-1, 1))
critic_input = torch.cat((states_full, actions), dim=1).to(device)
q = agent.critic(critic_input)
huber_loss = torch.nn.SmoothL1Loss()
critic_loss = huber_loss(q, y.detach())
critic_loss.backward()
#torch.nn.utils.clip_grad_norm_(agent.critic.parameters(), 0.5)
agent.critic_optimizer.step()
# -------------------------- update actor -------------------------- #
agent.actor_optimizer.zero_grad()
# make input to agent
# detach the other agents to save computation
# saves some time for computing derivative
q_input = [ self.maddpg_agent[i].actor(state) if i == agent_number \
else self.maddpg_agent[i].actor(state).detach()
for i, state in enumerate(states) ]
q_input = torch.cat(q_input, dim=1)
# combine all the actions and observations for input to critic
q_input2 = torch.cat((states_full, q_input), dim=1)
# get the policy gradient
actor_loss = -agent.critic(q_input2).mean()
actor_loss.backward()
#torch.nn.utils.clip_grad_norm_(agent.actor.parameters(),0.5)
agent.actor_optimizer.step()
# for TensorBoard
al = actor_loss.cpu().detach().item()
cl = critic_loss.cpu().detach().item()
logger.add_scalars('agent%i/losses' % agent_number, {
'critic loss': cl,
'actor_loss': al
}, self.iter)
def main():
seeding()
parallel_envs = 4
number_of_episodes = 1000
episode_length = 80
batchsize = 1000
save_interval = 1000
t = 0
# amplitude of OU noise, which slowly decreases to 0
noise = 2
noise_reduction = 0.9999
# how many episodes before update
episode_per_update = 2 * parallel_envs
log_path = os.getcwd() + "/log"
model_dir = os.getcwd() + "/model_dir"
os.makedirs(model_dir, exist_ok=True)
torch.set_num_threads(parallel_envs)
"""
`env` controls three agents, two blue, one red.
env.observation_space: [Box(14,), Box(14,), Box(14,)]
env.action_sapce: [Box(2,), Box(2,), Box(2,)]
Box(14,) can be broken down into 2+3*2+3*2=14
(2) location coordinates of the target landmark
(3*2) the three agents' positions w.r.t. the target landmark
(3*2) the three agents' velocities w.r.t. the target landmark
"""
env = envs.make_parallel_env(parallel_envs)
# keep 5000 episodes worth of replay
buffer = ReplayBuffer(int(5000 * episode_length))
# initialize policy and critic
maddpg = MADDPG()
logger = SummaryWriter(log_dir=log_path)
agent0_reward = []
agent1_reward = []
agent2_reward = []
# training loop
# show progressbar
import progressbar as pb
widget = [
'episode: ',
pb.Counter(), '/',
str(number_of_episodes), ' ',
pb.Percentage(), ' ',
pb.ETA(), ' ',
pb.Bar(marker=pb.RotatingMarker()), ' '
]
timer = pb.ProgressBar(widgets=widget, maxval=number_of_episodes).start()
# use keep_awake to keep workspace from disconnecting
for episode in keep_awake(range(0, number_of_episodes, parallel_envs)):
timer.update(episode)
reward_this_episode = np.zeros((parallel_envs, 3))
# Consult `env_wrapper.py` line 19.
all_obs = env.reset()
"""
`all_abs` is a list of size `parallel_envs`,
each item in the list is another list of size two,
first is env.observation_space: [Box(14,), Box(14,), Box(14,)],
second is [Box(14,)], which is added to faciliate training
https://goo.gl/Xtr6sF
`obs` and `obs_full` are both lists of size `parallel_envs`,
`obs` has the default observation space [Box(14,), Box(14,), Box(14,)]
`obs_full` has the compounded observation space [Box(14,)]
"""
obs, obs_full = transpose_list(all_obs)
# for calculating rewards for one episode - addition of all time steps
# save info or not
save_info = ((episode) % save_interval < parallel_envs
or episode == number_of_episodes - parallel_envs)
frames = []
tmax = 0
if save_info:
frames.append(env.render('rgb_array'))
for episode_t in range(episode_length):
t += parallel_envs
# explore = only explore for a certain number of steps
# action input needs to be transposed
actions = maddpg.act(transpose_to_tensor(obs), noise=noise)
noise *= noise_reduction
# `actions_array` has shape (3, parallel_envs, 2)
actions_array = torch.stack(actions).detach().numpy()
# `actions_for_env` has shape (parallel_envs, 3, 2), because
# input to `step` requires the first index to be `parallel_envs`
actions_for_env = np.rollaxis(actions_array, axis=1)
# step forward one frame
next_obs, next_obs_full, rewards, dones, info = \
env.step(actions_for_env)
# add data to buffer
transition = (obs, obs_full, actions_for_env, rewards, next_obs,
next_obs_full, dones)
buffer.push(transition)
reward_this_episode += rewards
obs, obs_full = next_obs, next_obs_full
# save gif frame
if save_info:
frames.append(env.render('rgb_array'))
tmax += 1
# update the target network `parallel_envs`=4 times
# after every `episode_per_update`=2*4
if len(buffer
) > batchsize and episode % episode_per_update < parallel_envs:
# update the local network for all agents, `a_i` refers to agent no.
for a_i in range(3):
samples = buffer.sample(batchsize)
maddpg.update(samples, a_i, logger)
# soft update the target network towards the actual networks
maddpg.update_targets()
for i in range(parallel_envs):
agent0_reward.append(reward_this_episode[i, 0])
agent1_reward.append(reward_this_episode[i, 1])
agent2_reward.append(reward_this_episode[i, 2])
if episode % 100 == 0 or episode == number_of_episodes - 1:
avg_rewards = [
np.mean(agent0_reward),
np.mean(agent1_reward),
np.mean(agent2_reward)
]
agent0_reward = []
agent1_reward = []
agent2_reward = []
for a_i, avg_rew in enumerate(avg_rewards):
logger.add_scalar('agent%i/mean_episode_rewards' % a_i,
avg_rew, episode)
# Saves the model.
save_dict_list = []
if save_info:
for i in range(3):
save_dict = {
'actor_params':
maddpg.maddpg_agent[i].actor.state_dict(),
'actor_optim_params':
maddpg.maddpg_agent[i].actor_optimizer.state_dict(),
'critic_params':
maddpg.maddpg_agent[i].critic.state_dict(),
'critic_optim_params':
maddpg.maddpg_agent[i].critic_optimizer.state_dict()
}
save_dict_list.append(save_dict)
torch.save(
save_dict_list,
os.path.join(model_dir, 'episode-{}.pt'.format(episode)))
# Save gif files.
imageio.mimsave(os.path.join(model_dir,
'episode-{}.gif'.format(episode)),
frames,
duration=.04)
env.close()
logger.close()
timer.finish()
def main():
seeding()
# number of training episodes.
number_of_episodes = 5000
episode_length = 1000
batchsize = 2000
t = 0
# amplitude of OU noise
# this slowly decreases to 0
noise = 2
noise_reduction = 0.9999
# how many episodes before update
episode_per_update = 2
log_path = os.getcwd() + "/log"
model_dir = os.getcwd() + "/model_dir"
os.makedirs(model_dir, exist_ok=True)
# env = UnityEnvironment('Tennis_Windows_x86_64/Tennis.exe')
env = UnityEnvironment('Tennis_Windows_x86_64/Tennis.exe',
no_graphics=True)
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
env_info = env.reset(train_mode=True)[brain_name]
num_agents = len(env_info.agents)
replay_episodes = 1000
buffer = ReplayBuffer(int(replay_episodes * episode_length))
# initialize policy and critic
maddpg = MADDPG()
# logger = SummaryWriter(log_dir=log_path)
agent0_reward = []
agent1_reward = []
# training loop
scores_deque = deque(maxlen=100)
scores = []
for episode in range(0, number_of_episodes):
reward_this_episode = np.zeros(num_agents)
env_info = env.reset(True)[brain_name]
state = env_info.vector_observations
obs = [[state[0], state[1]]]
obs_full = np.concatenate((state[0], state[1]))
#for calculating rewards for this particular episode - addition of all time steps
frames = []
tmax = 0
for episode_t in range(episode_length):
t += 1
# explore = only explore for a certain number of episodes
# action input needs to be transposed
actions = maddpg.act(transpose_to_tensor(obs), noise=noise)
noise *= noise_reduction
actions_array = torch.stack(actions).detach().numpy()
# transpose the list of list
# flip the first two indices
# input to step requires the first index to correspond to number of parallel agents
# actions_for_env = np.rollaxis(actions_array,1)
actions_for_env = np.clip(actions_array.flatten(), -1, 1)
# print(actions_for_env)
# step forward one frame
# next_obs, next_obs_full, rewards, dones, info = env.step(actions_for_env)
env_info = env.step(actions_for_env)[brain_name]
next_state = env_info.vector_observations
rewards = env_info.rewards
dones = env_info.local_done
next_obs = [[next_state[0], next_state[1]]]
next_obs_full = np.concatenate((next_state[0], next_state[1]))
# print(obs, obs_full, actions_for_env, rewards, next_obs, next_obs_full, dones
# add data to buffer
transition = ([obs], [obs_full], [actions_for_env], [rewards],
[next_obs], [next_obs_full], [dones])
buffer.push(transition)
reward_this_episode += rewards
obs, obs_full = next_obs, next_obs_full
if any(dones):
break
# update once after every episode_per_update
if len(buffer) > batchsize and episode % episode_per_update == 0:
for a_i in range(num_agents):
samples = buffer.sample(batchsize)
maddpg.update(samples, a_i)
maddpg.update_targets(
) #soft update the target network towards the actual networks
avg_rewards = np.mean(reward_this_episode, axis=0)
episode_reward = np.max(avg_rewards)
scores_deque.append(episode_reward)
scores.append(episode_reward)
print('\rEpisode {}\tAverage Score: {:.3f}\tEpisode Score: {:.3f}'.
format(episode, np.mean(scores_deque), episode_reward),
end="")
if (episode > 0
and episode % 100 == 0) or episode == number_of_episodes - 1:
print('\rEpisode {}\tAverage Score: {:.3f}\tEpisode Score: {:.3f}'.
format(episode, np.mean(scores_deque), episode_reward))
if np.mean(scores_deque) >= 0.5:
print('\nSuccess!')
break
#saving model
save_dict_list = []
for i in range(num_agents):
save_dict = {
'actor_params':
maddpg.maddpg_agent[i].actor.state_dict(),
'actor_optim_params':
maddpg.maddpg_agent[i].actor_optimizer.state_dict(),
'critic_params':
maddpg.maddpg_agent[i].critic.state_dict(),
'critic_optim_params':
maddpg.maddpg_agent[i].critic_optimizer.state_dict()
}
save_dict_list.append(save_dict)
torch.save(save_dict_list,
os.path.join(model_dir, 'episode-{}.pt'.format(episode)))
env.close()
fig = plt.figure()
ax = fig.add_subplot(111)
plt.ylabel('Score')
plt.xlabel('Episode #')
plt.plot(np.arange(1, len(scores) + 1), scores)
plt.savefig('tennis_score_history.png')
return scores
def main():
seeding(seed=SEED)
# number of parallel agents
parallel_envs = 1
# number of agents per environment
num_agents = 5
# initialize environment
torch.set_num_threads(parallel_envs)
env = envs.make_parallel_env(parallel_envs,
seed=SEED,
num_agents=num_agents,
benchmark=BENCHMARK)
# initialize policy and critic
maddpg = MADDPG(num_agents=num_agents,
discount_factor=GAMMA,
tau=TAU,
lr_actor=LR_ACTOR,
lr_critic=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
agents_reward = []
for n in range(num_agents):
agents_reward.append([])
# trained_checkpoint = r'E:\Ivan\UPC\UDACI TY\DRL_Nanodegree\Part4\MADDPG\032521_163018\model_dir\episode-59994.pt' #test1 2 agents
# trained_checkpoint = r'E:\Ivan\UPC\UDACITY\DRL_Nanodegree\Part4\MADDPG\032521_211315\model_dir\episode-59994.pt' #test1 2 agents
# trained_checkpoint = r'E:\Ivan\UPC\UDACITY\DRL_Nanodegree\Part4\MADDPG\032621_054252\model_dir\episode-36000.pt' #test1 2 agents
# trained_checkpoint = r'E:\Ivan\UPC\UDACITY\DRL_Nanodegree\Part4\MADDPG\032821_102717\model_dir\episode-99000.pt' #test1 6 agents
# trained_checkpoint = r'E:\Ivan\UPC\UDACITY\DRL_Nanodegree\Part4\MADDPG\032921_160324\model_dir\episode-99000.pt' #test2 6 agents pretrined
# trained_checkpoint = r'E:\Ivan\UPC\UDACITY\DRL_Nanodegree\Part4\MADDPG\033021_203450\model_dir\episode-73002.pt' # test2 6 agents pretrined
# trained_checkpoint = "c4-a4-n01-a01-old-two-noskip/model_dir/episode-59999.pt"
trained_checkpoint = "gat-huge1k/model_dir/episode-99000.pt"
aux = torch.load(trained_checkpoint, map_location=torch.device('cpu'))
for i in range(num_agents):
# load the weights from file
maddpg.maddpg_agent[i].actor.load_state_dict(aux[i]['actor_params'])
maddpg.maddpg_agent[i].critic.load_state_dict(aux[i]['critic_params'])
# Reset the environment
all_obs = env.reset()
# flip the first two indices
obs_roll = np.rollaxis(all_obs, 1)
obs = transpose_list(obs_roll)
scores = 0
t = 0
while True:
all_obs = env.reset()
# flip the first two indices
obs_roll = np.rollaxis(all_obs, 1)
obs = transpose_list(obs_roll)
scores = 0
t = 0
for _ in range(25):
env.render('rgb_array')
time.sleep(0.1)
t += 1
# select an action
actions = maddpg.act(transpose_to_tensor(obs), noise=0.)
actions_array = torch.stack(actions).detach().numpy()
actions_for_env = np.rollaxis(actions_array, 1)
# send all actions to the environment
next_obs, rewards, dones, info = env.step(actions_for_env)
# update the score (for each agent)
scores += np.sum(rewards)
print('\r\n Rewards at step %i = %.3f' % (t, scores))
# for displaying learned policies
# time.sleep(0.1)
# env.render()
# roll over states to next time step
obs = next_obs
# print("Score: {}".format(scores))
if np.any(dones):
print('done')
print('Next:')
env.close()
def main():
seeding(seed=SEED)
# number of parallel agents
parallel_envs = 1
# number of agents per environment
num_agents = 5
# number of training episodes.
# change this to higher number to experiment. say 30000.
number_of_episodes = 60000
episode_length = 35
# how many episodes to save policy and gif
save_interval = 1000
t = 0
scenario_name = "simple_spread_ivan"
# amplitude of OU noise
# this slowly decreases to 0
noise = 0.5 # was 2, try 0.5, 0.2
noise_reduction = 0.9999 # 0.999
#### DECAY
initial_noise = 0.1
decay = 0.01
# how many episodes before update
# episode_per_update = UPDATE_EVERY * parallel_envs
common_folder = time.strftime("/%m%d%y_%H%M%S")
log_path = os.getcwd() + common_folder + "/log"
model_dir = os.getcwd() + common_folder + "/model_dir"
os.makedirs(model_dir, exist_ok=True)
# initialize environment
# torch.set_num_threads(parallel_envs)
env = envs.make_parallel_env(parallel_envs, seed=3, benchmark=BENCHMARK)
# env = envs.make_env("simple_spread_ivan")
# initialize replay buffer
buffer = ReplayBuffer(int(BUFFER_SIZE))
# initialize policy and critic
maddpg = MADDPG(num_agents=num_agents,
discount_factor=GAMMA,
tau=TAU,
lr_actor=LR_ACTOR,
lr_critic=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
logger = SummaryWriter(log_dir=log_path)
agents_reward = []
for n in range(num_agents):
agents_reward.append([])
# agent0_reward = []
# agent1_reward = []
# agent2_reward = []
agent_info = [[[]]] # placeholder for benchmarking info
# training loop
# show progressbar
import progressbar as pb
widget = [
'\repisode: ',
pb.Counter(), '/',
str(number_of_episodes), ' ',
pb.Percentage(), ' ',
pb.ETA(), ' ',
pb.Bar(marker=pb.RotatingMarker()), ' '
]
timer = pb.ProgressBar(widgets=widget, maxval=number_of_episodes).start()
print('Starting iterations...')
for episode in range(0, number_of_episodes, parallel_envs):
timer.update(episode)
reward_this_episode = np.zeros((parallel_envs, num_agents))
all_obs = env.reset() #
# flip the first two indices
# ADD FOR WITHOUT PARALLEL ENV
# all_obs = np.expand_dims(all_obs, axis=0)
obs_roll = np.rollaxis(all_obs, 1)
obs = transpose_list(obs_roll)
# save info or not
save_info = ((episode) % save_interval < parallel_envs
or episode == number_of_episodes - parallel_envs)
frames = []
tmax = 0
# if save_info:
# frames.append(env.render('rgb_array'))
for episode_t in range(episode_length):
# get actions
# explore = only explore for a certain number of episodes
# action input needs to be transposed
actions = maddpg.act(transpose_to_tensor(obs), noise=noise)
noise = max(initial_noise * decay**(episode_t / 20000), 0.001)
# noise = max(noise*noise_reduction, 0.001)
actions_array = torch.stack(actions).detach().numpy()
# transpose the list of list
# flip the first two indices
# input to step requires the first index to correspond to number of parallel agents
actions_for_env = np.rollaxis(actions_array, 1)
# environment step
# step forward one frame
# next_obs, next_obs_full, rewards, dones, info = env.step(actions_for_env)
# ADD FOR WITHOUT PARALLEL ENV
# next_obs, rewards, dones, info = env.step(actions_for_env)
next_obs, rewards, dones, info = env.step(actions_for_env)
# rewards_sum += np.mean(rewards)
# collect experience
transition = (obs, actions_for_env, rewards, next_obs, dones)
buffer.push(transition)
reward_this_episode += rewards
# obs, obs_full = next_obs, next_obs_full
obs = next_obs
# increment global step counter
t += parallel_envs
# save gif frame
if save_info:
# frames.append(env.render('rgb_array'))
tmax += 1
# for benchmarking learned policies
if BENCHMARK:
for i, inf in enumerate(info):
agent_info[-1][i].append(inf['n'])
# update once after every episode_per_update
# if len(buffer) > BATCH_SIZE and episode % episode_per_update < parallel_envs:
if len(buffer) > BATCH_SIZE and episode % UPDATE_EVERY < parallel_envs:
for _ in range(UPDATE_TIMES):
for a_i in range(num_agents):
samples = buffer.sample(BATCH_SIZE)
maddpg.update(samples, a_i, logger)
maddpg.update_targets(
) # soft update the target network towards the actual networks
for i in range(parallel_envs):
for n in range(num_agents):
agents_reward[n].append(reward_this_episode[i, n])
# agent0_reward.append(reward_this_episode[i,0])
# agent1_reward.append(reward_this_episode[i,1])
# agent2_reward.append(reward_this_episode[i,2])
if episode % 100 == 0 or episode == number_of_episodes - 1:
# avg_rewards = [np.mean(agent0_reward), np.mean(agent1_reward), np.mean(agent2_reward)]
avg_rewards = []
for n in range(num_agents):
avg_rewards.append(np.mean(agents_reward[n]))
# agent0_reward = []
# agent1_reward = []
# agent2_reward = []
for a_i, avg_rew in enumerate(avg_rewards):
logger.add_scalar('agent%i/mean_episode_rewards' % a_i,
avg_rew, episode)
# saving model
save_dict_list = []
if save_info:
print('agent_info benchmark=', agent_info)
for i in range(5):
save_dict = {
'actor_params':
maddpg.maddpg_agent[i].actor.state_dict(),
'actor_optim_params':
maddpg.maddpg_agent[i].actor_optimizer.state_dict(),
'critic_params':
maddpg.maddpg_agent[i].critic.state_dict(),
'critic_optim_params':
maddpg.maddpg_agent[i].critic_optimizer.state_dict()
}
save_dict_list.append(save_dict)
torch.save(
save_dict_list,
os.path.join(model_dir, 'episode-{}.pt'.format(episode)))
# save gif files
# imageio.mimsave(os.path.join(model_dir, 'episode-{}.gif'.format(episode)),
# frames, duration=.04)
env.close()
logger.close()
timer.finish()
