def maddpg(n_episodes = 5000):
#PARAMETERS:
noise = 2
batch_size = 256
update_every = 1
agent = MADDPG(discount_factor = 0.99, tau = 0.02, batch_size = batch_size)
buff = ReplayBuffer(10000)
for i_episode in range(n_episodes):
env_info = env.reset(train_mode = True)[brain_name]
state = env_info.vector_observations
state = torch.from_numpy(np.array(state)).float().unsqueeze(0)
score = np.zeros(num_agents)
t = 0
while True:
actions = agent.act(state, noise)
noise *= 0.9999
actions_array = torch.stack(actions).detach().numpy()
env_info = env.step(actions_array)[brain_name]
next_state = env_info.vector_observations
next_state = torch.from_numpy(np.array(next_state)).float().unsqueeze(0)
reward = np.array(env_info.rewards).reshape(1, -1)
dones = np.array(env_info.local_done).reshape(1, -1)
actions_array = actions_array.reshape(1, -1)
buff.push((state, actions_array, reward, next_state, dones))
if len(buff) > batch_size and t % update_every == 0:
for i in range(2):
samples = buff.sample(batch_size)
agent.update(samples, i, noise)
agent.update_targets()
t += 1
score += reward[0]
state = next_state
if np.any(dones):
break
scores_window.append(np.max(score))
scores.append(np.max(score))
print('\rEpisode {}\tAverage Score: {:.3f}'.format(i_episode, np.mean(scores_window)), end = "")
if i_episode % 100:
for i in range(2):
torch.save(agent.maddpg_agent[i].actor.state_dict(), 'bin/checkpoint_actor{}.pth'.format(i))
torch.save(agent.maddpg_agent[i].critic.state_dict(), 'bin/checkpoint_critic{}.pth'.format(i))
if np.mean(scores_window) >= 0.5:
print('\nEnvironment solved in {:d} episodes!\tAverage Score: {:.3f}'.format(i_episode - 100, np.mean(scores_window)))
for i in range(2):
torch.save(agent.maddpg_agent[i].actor.state_dict(), 'bin/actor{}_finished.pth'.format(i))
torch.save(agent.maddpg_agent[i].critic.state_dict(), 'bin/critic{}_finished.pth'.format(i))
break
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 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()
if any(dones):
break
if episode_i > HARD_NOISE_STEPS:
hard_noise_reigime = False
# POTENTIALLY START TAKING SAMPLES TO TRAIN FROM EXPERIENCE BUFFER
if len(buffer) > MIN_BUFFER_SIZE:
update_flag = "u"
for _ in range(N_BATCHES_PER_UPDATE):
for agent_i in range(N_AGENTS):
# samples = buffer.sample(3)
samples = buffer.sample(BATCH_SIZE)
maddpg.update(samples, agent_i)
if UPDATE_TARGET_AFTER_EACH_BATCH:
maddpg.update_targets()
if not UPDATE_TARGET_AFTER_EACH_BATCH:
maddpg.update_targets()
else:
update_flag = " "
# UPDATE EPISODE AND ROLLING MEAN SCORES
agg_reward_this_episode = np.max(rewards_this_episode)
rewards_deque.append(agg_reward_this_episode)
rolling_mean_reward = np.mean(rewards_deque)
history.append(agg_reward_this_episode)
history_rolling_mean.append(rolling_mean_reward)
# MONITOR PROGRESS IN TENSORBOARD
if logger is not None:
def main():
seeding()
number_of_episodes = 20000
episode_length = 1000
batchsize = 256
save_interval = 1000
rewards_deque = deque(maxlen=100)
rewards_all = []
noise = 1.0
noise_reduction = 1.0
log_path = os.getcwd() + "/log"
model_dir = os.getcwd() + "/model_dir"
os.makedirs(model_dir, exist_ok=True)
""" Info about the UnityEnvironment
brain_name: 'TennisBrain'
brain: ['brain_name', 'camera_resolutions',
'num_stacked_vector_observations', 'number_visual_observations',
'vector_action_descriptions', 'vector_action_space_size',
'vector_action_space_type', 'vector_observation_space_size',
'vector_observation_space_type']]
"""
env = UnityEnvironment(file_name="Tennis.app")
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
buffer = ReplayBuffer(int(1e5))
# initialize policy and critic
maddpg = MADDPG()
logger = SummaryWriter(log_dir=log_path)
# ------------------------------ training ------------------------------ #
# 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()
for episode in range(1, number_of_episodes + 1):
timer.update(episode)
rewards_this_episode = np.zeros((2, ))
""" Info about the UnityEnvironment
env_info: ['agents', 'local_done', 'max_reached', 'memories',
'previous_text_actions', 'previous_vector_actions', 'rewards',
'text_observations', 'vector_observations', 'visual_observations']
actions: List(num_agents=2, action_size=2)
states: List((24,), (24,))
rewards: List(2,)
dones: List(2,)
"""
env_info = env.reset(train_mode=True)[brain_name]
states = env_info.vector_observations
for episode_t in range(episode_length):
# reset the OUNoise for each agent.
for i in range(2):
maddpg.maddpg_agent[i].noise.reset()
actions = maddpg.act(states, noise=noise)
env_info = env.step(actions)[brain_name]
noise *= noise_reduction
next_states = env_info.vector_observations
rewards = env_info.rewards
dones = env_info.local_done
# add data to buffer
transition = (states, actions, rewards, next_states, dones)
buffer.push(transition)
rewards_this_episode += rewards
states = next_states
if any(dones):
break
# update the local and target network
if len(buffer) > batchsize:
# update the local network
for _ in range(5):
for a_i in range(2):
samples = buffer.sample(batchsize)
maddpg.update(samples, a_i, logger)
# soft update the target network
maddpg.update_targets()
rewards_all.append(rewards_this_episode)
rewards_deque.append(np.max(rewards_this_episode))
average_score = np.mean(rewards_deque)
# --------------------- Logging for TensorBoard --------------------- #
logger.add_scalars('rewards', {
'agent0': rewards_this_episode[0],
'agent1': rewards_this_episode[1]
}, episode)
logger.add_scalars('global', {
'score': np.max(rewards_this_episode),
'average_score': average_score
}, episode)
# -------------------------- Save the model -------------------------- #
save_dict_list = []
if episode % save_interval == 0 or average_score >= 0.5:
for i in range(2):
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)))
if average_score >= 3.0:
print('\nEnvironment solved in {} episodes!'.format(episode -
100))
print('\nAverage Score: {:.2f}'.format(average_score))
break
env.close()
logger.close()
timer.finish()
def train(env,
model_path='model_dir',
number_of_episodes=50000,
episode_length=500):
noise = 1.0
noise_reduction = 1.0
batchsize = 256
model_dir = os.getcwd() + "/" + model_path
model_files = glob.glob(model_dir + "/*.pt")
for file in model_files:
os.remove(file)
os.makedirs(model_dir, exist_ok=True)
buffer = ReplayBuffer(int(1e5))
rewards_deque = deque(maxlen=100)
rewards_total = []
# initialize policy and critic
maddpg = MADDPG()
for episode in range(1, number_of_episodes + 1):
rewards_this_episode = np.asarray([0.0, 0.0])
env_info = env.reset(train_mode=True)[brain_name]
obs = env_info.vector_observations
for episode_t in range(episode_length):
actions = maddpg.act(obs, noise=noise)
noise *= noise_reduction
env_info = env.step(actions)[brain_name]
next_obs = env_info.vector_observations
rewards = env_info.rewards
dones = env_info.local_done
# add data to buffer
transition = (obs, actions, rewards, next_obs, dones)
buffer.push(transition)
rewards_this_episode += rewards
obs = next_obs
if any(dones):
break
# update once after every episode_per_update
if len(buffer) > batchsize * 4:
for _ in range(4):
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
rewards_total.append(np.max(rewards_this_episode))
rewards_deque.append(rewards_total[-1])
average_score = np.mean(rewards_deque)
print(episode, rewards_this_episode, rewards_total[-1], average_score)
# saving model
save_dict_list = []
if episode % 1000 == 0:
for i in range(2):
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)))
torch.save(maddpg.maddpg_agent[0].actor.state_dict(),
'actor0.pt')
torch.save(maddpg.maddpg_agent[1].actor.state_dict(),
'actor1.pt')
torch.save(maddpg.maddpg_agent[0].critic.state_dict(),
'critic0.pt')
torch.save(maddpg.maddpg_agent[1].critic.state_dict(),
'critic1.pt')
return rewards_total
def main():
env_info = env.reset(train_mode=False)[brain_name]
num_agents = len(env_info.agents)
print('Number of agents:', num_agents)
# size of each action
action_size = brain.vector_action_space_size
print('Size of each action:', action_size)
# examine the state space
states = env_info.vector_observations
state_size = states.shape[1]
seeding()
# number of parallel agents
#parallel_envs = num_agents
# number of training episodes.
# change this to higher number to experiment. say 30000.
number_of_episodes = 10000
update_actor_after = 100
update_actor_every = 2
episode_length = 100
batchsize = 100
# how many episodes to save policy and gif
save_interval = 1000
t = 0
LR_ACTOR = 1e-5
LR_CRITIC = 3e-3
# amplitude of OU noise
# this slowly decreases to 0
noise = 1.0
noise_reduction = 0.999999
# how many episodes before update
episode_per_update = 1
no_of_updates_perTime = 1
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(10 * episode_length))
# initialize policy and critic
maddpg = MADDPG(lr_actor=LR_ACTOR, lr_critic=LR_CRITIC)
#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 range(0, number_of_episodes):
timer.update(episode)
env_info = env.reset(
train_mode=False)[brain_name] # reset the environment
states = env_info.vector_observations # get the current state (for each agent)
scores = np.zeros(num_agents) # initialize the score (for each agent)
reward_this_episode = np.zeros((1, num_agents))
#all_obs = env.reset() #
obs = states
obs_full = np.concatenate((states[0], states[1]))
#for calculating rewards for this particular episode - addition of all time steps
# save info or not
save_info = ((episode) % save_interval < 1
or episode == number_of_episodes - 1)
tmax = 0
#resetting noise
for i in range(num_agents):
maddpg.maddpg_agent[i].noise.reset()
for episode_t in range(episode_length):
t += 1
update_act = True if (episode > update_actor_after or episode %
update_actor_every == 0) else False
# explore = only explore for a certain number of episodes
# action input needs to be transposed
actions = maddpg.act(transpose_to_tensorAsitis(obs),
noise=noise,
batch=False)
noise *= noise_reduction
actions_array = torch.stack(actions).cpu().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
env_info = env.step(actions_for_env)[brain_name]
next_states = env_info.vector_observations # get next state (for each agent)
rewards = env_info.rewards # get reward (for each agent)
dones = env_info.local_done # see if episode finished
scores += env_info.rewards
rewards_for_env = np.hstack(rewards)
obs = states
obs_full = np.concatenate((states[0], states[1]))
next_obs = next_states
next_obs_full = np.concatenate((next_states[0], next_states[1]))
# add data to buffer
transition = (np.array([obs]), np.array([obs_full]),
np.array([actions_for_env]),
np.array([rewards_for_env]), np.array([next_obs]),
np.array([next_obs_full]),
np.array([dones], dtype='float'))
buffer.push(transition)
reward_this_episode += rewards
obs, obs_full = next_obs, next_obs_full
# update once after every episode_per_update
if len(buffer) > batchsize and episode % episode_per_update == 0:
for _ in range(no_of_updates_perTime):
for a_i in range(num_agents):
samples = buffer.sample(batchsize)
#updating the weights of the n/w
maddpg.update(samples, a_i, update_actor=update_act)
maddpg.update_targets(
) #soft update the target network towards the actual networks
if np.any(dones):
# if the episode is done the loop is break to the next episode
break
for i in range(num_agents):
agent0_reward.append(reward_this_episode[0, 0])
agent1_reward.append(reward_this_episode[0, 1])
if episode % 100 == 0 or episode == number_of_episodes - 1:
avg_rewards = [np.mean(agent0_reward), np.mean(agent1_reward)]
agent0_reward = []
agent1_reward = []
for a_i, avg_rew in enumerate(avg_rewards):
#logger.add_scalar('agent%i/mean_episode_rewards' % a_i, avg_rew, episode)
print('agent%i/mean_episode_rewards' % a_i, avg_rew, episode)
#saving model
save_dict_list = []
if save_info:
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)))
# save gif files
#imageio.mimsave(os.path.join(model_dir, 'episode-{}.gif'.format(episode)),
#frames, duration=.04)
timer.finish()
def main():
##########
# CONFIG #
##########
# Target Reward
tgt_score = 0.5
# Device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Seed
seed = 7
seeding(seed)
# Model Architecture
# Actor
hidden_in_actor = 256
hidden_out_actor = 128
lr_actor = 1e-4
# Critic
hidden_in_critic = 256
hidden_out_critic = 128
lr_critic = 3e-4
weight_decay_critic = 0
# Episodes
number_of_episodes = 10000
episode_length = 2000
# Buffer
buffer_size = int(1e6)
batchsize = 512
# Agent Update Frequency
episode_per_update = 1
# Rewards Discounts Factor
discount_factor = 0.95
# Soft Update Weight
tau = 1e-2
# Noise Process
noise_factor = 2
noise_reduction = 0.9999
noise_floor = 0.0
# Window
win_len = 100
# Save Frequency
save_interval = 200
# Logger
log_path = os.getcwd() + "/log"
logger = SummaryWriter(log_dir=log_path)
# Model Directory
model_dir = os.getcwd() + "/model_dir"
os.makedirs(model_dir, exist_ok=True)
# Load Saved Model
load_model = False
####################
# Load Environment #
####################
env = UnityEnvironment(file_name="./Tennis_Linux_NoVis/Tennis.x86_64")
# Get brain
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
print('Brain Name:', brain_name)
# Reset the environment
env_info = env.reset(train_mode=True)[brain_name]
# Number of Agents
num_agents = len(env_info.agents)
print('Number of agents:', num_agents)
# size of each action
action_size = brain.vector_action_space_size
print('Size of each action:', action_size)
# examine the state space
states = env_info.vector_observations
state_size = states.shape[1]
print('There are {} agents. Each observes a state with length: {}'.format(
states.shape[0], state_size))
####################
# 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()
start = time.time()
###############
# Multi Agent #
###############
maddpg = MADDPG(state_size, action_size, num_agents, hidden_in_actor,
hidden_out_actor, lr_actor, hidden_in_critic,
hidden_out_critic, lr_critic, weight_decay_critic,
discount_factor, tau, seed, device)
if load_model:
load_dict_list = torch.load(os.path.join(model_dir,
'episode-saved.pt'))
for i in range(num_agents):
maddpg.maddpg_agent[i].actor.load_state_dict(
load_dict_list[i]['actor_params'])
maddpg.maddpg_agent[i].actor_optimizer.load_state_dict(
load_dict_list[i]['actor_optim_params'])
maddpg.maddpg_agent[i].critic.load_state_dict(
load_dict_list[i]['critic_params'])
maddpg.maddpg_agent[i].critic_optimizer.load_state_dict(
load_dict_list[i]['critic_optim_params'])
#################
# Replay Buffer #
#################
rebuffer = ReplayBuffer(buffer_size, seed, device)
#################
# TRAINING LOOP #
#################
# initialize scores
scores_history = []
scores_window = deque(maxlen=save_interval)
# i_episode = 0
for i_episode in range(number_of_episodes):
timer.update(i_episode)
# Reset Environmet
env_info = env.reset(train_mode=True)[brain_name]
states = env_info.vector_observations
scores = np.zeros(num_agents)
# Reset Agent
maddpg.reset()
# episode_t = 0
for episode_t in range(episode_length):
# Explore with decaying noise factor
actions = maddpg.act(states, noise_factor=noise_factor)
env_info = env.step(actions)[brain_name] # Environment reacts
next_states = env_info.vector_observations # get the next states
rewards = env_info.rewards # get the rewards
dones = env_info.local_done # see if episode has finished
###################
# Save Experience #
###################
rebuffer.add(states, actions, rewards, next_states, dones)
scores += rewards
states = next_states
if any(dones):
break
scores_history.append(np.max(scores)) # save most recent score
scores_window.append(np.max(scores)) # save most recent score
avg_rewards = np.mean(scores_window)
noise_factor = max(noise_floor, noise_factor *
noise_reduction) # Reduce Noise Factor
#########
# LEARN #
#########
if len(rebuffer) > batchsize and i_episode % episode_per_update == 0:
for a_i in range(num_agents):
samples = rebuffer.sample(batchsize)
maddpg.update(samples, a_i, logger)
# Soft Update
maddpg.update_targets()
##################
# Track Progress #
##################
if i_episode % save_interval == 0 or i_episode == number_of_episodes - 1:
logger.add_scalars('rewards', {
'Avg Reward': avg_rewards,
'Noise Factor': noise_factor
}, i_episode)
print(
'\nElapsed time {:.1f} \t Update Count {} \t Last Episode t {}'
.format((time.time() - start) / 60, maddpg.update_count,
episode_t),
'\nEpisode {} \tAverage Score: {:.2f} \tNoise Factor {:2f}'.
format(i_episode, avg_rewards, noise_factor),
end="\n")
##############
# Save Model #
##############
save_info = ((i_episode) % save_interval == 0
or i_episode == number_of_episodes)
if save_info:
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-Latest.pt'))
pd.Series(scores_history).to_csv(
os.path.join(model_dir, "scores.csv"))
# plot the scores
rolling_mean = pd.Series(scores_history).rolling(win_len).mean()
fig = plt.figure()
ax = fig.add_subplot(111)
plt.plot(np.arange(len(scores_history)), scores_history)
plt.axhline(y=tgt_score, color='r', linestyle='dashed')
plt.plot(rolling_mean, lw=3)
plt.ylabel('Score')
plt.xlabel('Episode #')
# plt.show()
fig.savefig(os.path.join(model_dir, 'Average_Score.pdf'))
fig.savefig(os.path.join(model_dir, 'Average_Score.jpg'))
plt.close()
if avg_rewards > tgt_score:
logger.add_scalars('rewards', {
'Avg Reward': avg_rewards,
'Noise Factor': noise_factor
}, i_episode)
print(
'\nElapsed time {:.1f} \t Update Count {} \t Last Episode t {}'
.format((time.time() - start) / 60, maddpg.update_count,
episode_t),
'\nEpisode {} \tAverage Score: {:.2f} \tNoise Factor {:2f}'.
format(i_episode, avg_rewards, noise_factor),
end="\n")
break
env.close()
logger.close()
timer.finish()
def main():
seeding()
# number of parallel agents
env = UnityEnvironment(file_name="Tennis.x86_64")
env_name = 'Tennis'
# get the default brain
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
env_info = env.reset(train_mode=True)[brain_name]
# number of agents
num_agents = len(env_info.agents)
# size of each action
action_size = brain.vector_action_space_size
# examine the state space
states = env_info.vector_observations
state_size = states.shape[-1]
# number of training episodes.
# change this to higher number to experiment. say 30000.
number_of_episodes = 10000
episode_length = 10000
batchsize = 128
# amplitude of OU noise
# this slowly decreases to 0
noise = 1
noise_reduction = 0.9999
log_path = os.getcwd() + "/log"
model_dir = os.getcwd() + "/model_dir"
os.makedirs(model_dir, exist_ok=True)
# initialize memory buffer
buffer = ReplayBuffer(int(500000), batchsize, 0)
# initialize policy and critic
maddpg = MADDPG(state_size,
action_size,
num_agents,
seed=12345,
discount_factor=0.95,
tau=0.02)
#how often to update the MADDPG model
episode_per_update = 2
# training loop
PRINT_EVERY = 5
scores_deque = deque(maxlen=100)
# holds raw scores
scores = []
# holds avg scores of last 100 epsiodes
avg_last_100 = []
threshold = 0.5
# use keep_awake to keep workspace from disconnecting
for episode in range(number_of_episodes):
env_info = env.reset(
train_mode=True)[brain_name] # reset the environment
state = env_info.vector_observations # get the current state (for each agent)
episode_reward_agent0 = 0
episode_reward_agent1 = 0
for agent in maddpg.maddpg_agent:
agent.noise.reset()
for episode_t in range(episode_length):
actions = maddpg.act(torch.tensor(state, dtype=torch.float),
noise=noise)
noise *= noise_reduction
actions_array = torch.stack(actions).detach().numpy()
env_info = env.step(actions_array)[brain_name]
next_state = env_info.vector_observations
reward = env_info.rewards
done = env_info.local_done
episode_reward_agent0 += reward[0]
episode_reward_agent1 += reward[1]
# add data to buffer
'''
I can either hstack or concat two states here or do it in the update function in MADDPG
However I think it's easier to do it here, since in the update function I have batch_size to deal with
Although the replay buffer would have to hold more data by preprocessing and creating 2 new variables that
hold essentially the same info as state, and next_state, but just concatenated.
'''
full_state = np.concatenate((state[0], state[1]))
full_next_state = np.concatenate((next_state[0], next_state[1]))
buffer.add(state, full_state, actions_array, reward, next_state,
full_next_state, done)
state = next_state
# update once after every episode_per_update
if len(buffer) > batchsize and episode % episode_per_update == 0:
for i in range(num_agents):
samples = buffer.sample()
maddpg.update(samples, i)
maddpg.update_targets(
) # soft update the target network towards the actual networks
if np.any(done):
#if any of the agents are done break
break
episode_reward = max(episode_reward_agent0, episode_reward_agent1)
scores.append(episode_reward)
scores_deque.append(episode_reward)
avg_last_100.append(np.mean(scores_deque))
# scores.append(episode_reward)
print('\rEpisode {}\tAverage Score: {:.4f}\tScore: {:.4f}'.format(
episode, avg_last_100[-1], episode_reward),
end="")
if episode % PRINT_EVERY == 0:
print('\rEpisode {}\tAverage Score: {:.4f}'.format(
episode, avg_last_100[-1]))
# saving successful model
#training ends when the threshold value is reached.
if avg_last_100[-1] >= threshold:
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)))
# plots graphs
raw_score_plotter(scores)
plotter(env_name, len(scores), avg_last_100, threshold)
break
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
# 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()
