def __init__(self, state_size, action_size, seed):
super(MADDPG, self).__init__()
self.maddpg_agents = [
DDPGAgent(state_size, action_size, 1 * seed),
DDPGAgent(state_size, action_size, 2 * seed)
]
self.memory = ReplayBuffer(BUFFER_SIZE, BATCH_SIZE, seed)
def __init__(self, num_agents=2, random_seed=1): #np.random.randint(1000)
super(MADDPG, self).__init__()
self.maddpg_agent = [
DDPGAgent(24, 16, 8, 2, 52, 42, 24, random_seed),
DDPGAgent(24, 16, 8, 2, 52, 42, 24, random_seed)
]
self.num_agents = num_agents
# Replay memory
action_size = 2
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
def __init__(self,
num_agents,
local_obs_dim,
local_action_size,
global_obs_dim,
global_action_size,
discount_factor=0.95,
tau=0.02,
device=device):
super(MADDPG, self).__init__()
# store configuration parameters
self.device = device
self.discount_factor = discount_factor
self.tau = tau
self.num_agents = num_agents
# create maddgp agent
self.maddpg_agent = [
DDPGAgent(num_agents,
local_obs_dim,
local_action_size,
global_obs_dim,
global_action_size,
device=self.device) for _ in range(num_agents)
]
# iteration counter
self.iter = 0
def __init__(self,
numAgents,
state_size,
action_size,
random_seed,
batch_size=BATCH_SIZE,
buffer_size=BUFFER_SIZE,
use_batch_norm=USE_BATCH_NORM):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
"""
self.agents = [
DDPGAgent(i, numAgents, state_size, action_size, random_seed,
use_batch_norm) for i in range(numAgents)
]
self.numAgents = numAgents
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
# Replay memory
self.memory = ReplayBuffer(action_size, buffer_size, batch_size,
random_seed)
self.batch_size = batch_size
def __init__(self,
state_size,
action_size,
numAgent,
random_seed,
epsilon=1,
epsilonDecay=0.995,
minEpsilon=0.00):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
self.epsilon = epsilon
self.epsilonDecay = epsilonDecay
self.minEpsilon = minEpsilon
self.numAgent = numAgent
self.agents = [
DDPGAgent(state_size, action_size, random_seed)
for i in range(numAgent)
]
self.sharedMemory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
def __init__(self,
num_agents,
state_size,
action_size,
hidden_layers,
seed,
gamma=GAMMA,
tau=TAU,
lr_actor=LR_ACTOR,
lr_critic=LR_CRITIC,
weight_decay=WEIGHT_DECAY,
buffer_size=BUFFER_SIZE,
batch_size=BATCH_SIZE):
"""Initialize MADDPG agent."""
super(MADDPG, self).__init__()
self.seed = random.seed(seed)
self.num_agents = num_agents
self.state_size = state_size
self.action_size = action_size
self.gamma = gamma
self.tau = tau
self.lr_actor = lr_actor
self.lr_critic = lr_critic
self.weight_decay = weight_decay
self.buffer_size = buffer_size
self.batch_size = batch_size
self.agents = [DDPGAgent(state_size, action_size, hidden_layers, gamma, \
tau, lr_actor, lr_critic, weight_decay, seed) \
for _ in range(num_agents)]
self.replay_buffer = ReplayBuffer(num_agents, buffer_size, batch_size)
def __init__(self, state_size, action_size, n_agents, random_seed=1):
self.actor_local = Actor(state_size, action_size, random_seed)
self.actor_target = Actor(state_size, action_size, random_seed)
self.ddpg_agents = [
DDPGAgent(state_size, action_size, self.actor_local,
self.actor_target, random_seed) for _ in range(n_agents)
]
def __init__(self, config):
self.config = config
if config.shared_replay_buffer:
self.memory = config.memory()
self.config.memory = self.memory
self.ddpg_agents = [
DDPGAgent(self.config) for _ in range(config.num_agents)
]
self.t_step = 0
def __init__(self, state_size, action_size, num, seed):
# shared memory for all agents
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed,
device)
# define each agent
self.ddpg_agents = [
DDPGAgent(state_size, action_size, seed, self.memory, device)
for _ in range(num)
]
self.t_step = 0
self.num_agents = num
def test(args):
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)
print('Number of agents:', num_agents)
# dim of each action
action_size = brain.vector_action_space_size
print('Size of each action:', action_size)
# dim of the state space
states = env_info.vector_observations
state_size = states.shape[1]
agent_1 = DDPGAgent(state_size, action_size)
#agent_2 = DDPGAgent(state_size, action_size)
agent_2 = TD3Agent(state_size, action_size)
agent_1_path = '../results/td3_opponent/00_best_td3_model.checkpoint'
agent_2_path = '../results/ddgp_solo/01_best_model.checkpoint'
agent = MAAC(state_size, action_size, agent_2, agent_1, False, False)
agent.load(agent_1_path, 0)
agent.load(agent_2_path, 1)
test_scores = []
for i_episode in tqdm(range(1, 1 + args.test_n_run)):
# initialize the scores
scores = np.zeros(num_agents)
env_info = env.reset(
train_mode=True)[brain_name] # reset the environment
states = env_info.vector_observations # get the current states
dones = [False] * num_agents
while not np.any(dones):
actions = agent.act(states) # select actions
# send the actions to the environment
env_info = env.step(actions)[brain_name]
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
scores += rewards # update the scores
# roll over the states to next time step
states = next_states
test_scores.append(np.max(scores))
avg_score = sum(test_scores) / len(test_scores)
print("Test Score: {}".format(avg_score))
return avg_score
def train_ddpg_agent_job(config):
if config.render_game:
env = UnityEnvironment(
file_name="./resources/Tennis_Linux/Tennis.x86_64")
else:
env = UnityEnvironment(
file_name="./resources/Tennis_Linux_NoVis/Tennis.x86_64")
brain_name = env.brain_names[0]
brain = env.brains[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
print(states)
state_size = states.shape[1]
print('There are {} agents. Each observes a state with length: {}'.format(
states.shape[0], state_size))
print('The state for the first agent looks like:\n', states[0])
# Train the agent
agent = DDPGAgent(state_size, action_size, config.random_seed,
config.buffer_size, config.batch_size, config.gamma,
config.tau, config.lr_actor, config.lr_critic,
config.weight_decay, config.sigma, config.actor_nn_size,
config.critic_nn_size, config.batch_norm,
config.clip_grad_norm)
scores, avg_scores, std, save_path = train(agent, env, config.n_episodes,
config.score_window_size,
config.print_every,
config.max_score,
config.damp_exploration_noise)
config.dump(save_path + 'config.yml')
env.close()
return scores, avg_scores, std
def __init__(self, n_agents, state_size, action_size, seed=0):
super(MADDPGAgent, self).__init__()
self.n_agents = n_agents
self.maddpg_agent = [
DDPGAgent(state_size, action_size, n_agents, seed + i)
for i in range(n_agents)
]
# Replay memory
self.memory = ReplayBuffer((n_agents, action_size), BUFFER_SIZE,
BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
# Noise parameters with exponential decay
self.noise = INITIAL_NOISE
self.noise_decay = NOISE_DECAY
def run():
env = gym.make('Pendulum-v0')
seed = 30
env.seed(seed)
agent = DDPGAgent(seed=seed,
n_state=env.observation_space.shape[0],
n_action=env.action_space.shape[0])
'''
agent = Agent(state_size=env.observation_space.shape[0],
action_size=env.action_space.shape[0], random_seed=seed)
'''
episodes_n = 2000
steps_max = 300
scores = []
print_every = 100
scores_deque = deque(maxlen=print_every)
for i_episode in range(1, episodes_n):
state = env.reset()
agent.reset()
score = 0
done_step = 0
for step in range(steps_max):
action = agent.act(state)
state_next, reward, done, meta = env.step(action)
agent.step(state, action, reward, state_next, done)
state = state_next
score += reward
done_step += 1
if done:
break
scores.append(score)
scores_deque.append(score)
print_line(i_episode, scores_deque, end="")
if i_episode % print_every == 0:
print_line(i_episode, scores_deque, end="\n")
return scores
def __init__(self, num_agents, state_size, action_size):
"""Initialize a MADDPGAgent wrapper.
Params
======
num_agents (int): the number of agents in the environment
state_size (int): dimension of each state
action_size (int): dimension of each action
"""
self.num_agents = num_agents
self.state_size = state_size
self.action_size = action_size
self.agents = [DDPGAgent(state_size, action_size, i+1) for i in range(num_agents)]
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE)
# Will help to decide when to update the model weights
self.t_step = 0
# Directory where to save the model
self.model_dir = os.getcwd() + "/saved_models"
os.makedirs(self.model_dir, exist_ok=True)
def __init__(self, num_agents, state_size, action_size, random_seed=None):
super(MultiAgentDDPG, self).__init__()
"""Initialize an Agent object.
Params
======
num_agents (int): number of agents
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
"""
self.num_agents = num_agents
self.state_size = state_size
self.action_size = action_size
if random_seed == None:
random_seed = random.randint(1, 1000)
self.seed = random.seed(random_seed)
self.maddpg_agent = [DDPGAgent(state_size=self.state_size, action_size=self.action_size, random_seed=self.seed)\
for i in range(self.num_agents)]
self.iter = 0
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)
action_size = brain.vector_action_space_size
state = env_info.vector_observations
state_size = state.shape[1]
# create agent
agent1 = DDPGAgent(nS=state_size,
nA=action_size,
lr_actor=0.0005,
lr_critic=0.0005,
gamma=0.99,
batch_size=60,
tau=0.001,
memory_length=int(1e6),
no_op=int(1e3),
net_update_rate=1,
std_initial=0.15,
std_final=0.025,
std_decay_frames=200000)
# create agent
agent2 = DDPGAgent(nS=state_size,
nA=action_size,
lr_actor=0.0005,
lr_critic=0.0005,
gamma=0.99,
batch_size=60,
tau=0.001,
def run(env,
device,
episodes,
experiment_name,
update_rate,
action_size,
state_size,
brain_name,
epsilon_start=1.0,
epsilon_min=0.05,
epsilon_decay=0.995,
max_score=30.,
num_agents=1):
epsilon = epsilon_start
agent = DDPGAgent(state_space=state_size,
action_space=action_size,
buffer_size=int(1e5),
batch_size=512,
learning_rate_actor=0.001,
learning_rate_critic=0.001,
update_rate=update_rate,
gamma=0.995,
tau=0.001,
device=device,
seed=5,
num_agents=num_agents)
score_window = deque(maxlen=100)
all_scores = []
tb_writer = SummaryWriter('{}/{}'.format('logs', experiment_name))
for episode in range(episodes):
agent.reset()
scores = np.zeros(num_agents)
dones = np.zeros((num_agents), dtype=bool)
env_info = env.reset(train_mode=True)[brain_name]
states = env_info.vector_observations
while not np.any(dones):
actions = agent.act(states, epsilon)
actions = np.clip(actions, -1, 1) # all actions between -1 and 1
env_info = env.step(actions)[
brain_name] # send all actions to tne environment
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 # update the score (for each agent)
for state, action, reward, next_state, done in zip(
states, actions, rewards, next_states, dones):
agent.step(state, action, reward, next_state, done)
states = next_states
episode_score = np.mean(scores)
score_window.append(episode_score)
all_scores.append(episode_score)
#decay after each episode
if episode % 10 == 0:
epsilon = max(epsilon_min, epsilon * epsilon_decay)
print('\rEpisode: {}\tAverage Score: {}'.format(
episode, np.mean(score_window)),
end="")
if episode % 100 == 0:
tb_writer.add_scalar('Episode_Accum_score', np.mean(score_window),
episode)
print('\rEpisode: {}\tAverage Score: {}'.format(
episode, np.mean(score_window)))
if np.mean(score_window) >= max_score:
torch.save(agent.actor_local_network.state_dict(),
'actor_checkpoint_{}.pth'.format(experiment_name))
torch.save(agent.critic_local_network.state_dict(),
'critic_checkpoint_{}.pth'.format(experiment_name))
break
env = UnityEnvironment(
file_name="./resources/Tennis_Linux/Tennis.x86_64")
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
env_info = env.reset(train_mode=False)[brain_name]
num_agents = len(env_info.agents)
action_size = brain.vector_action_space_size
states = env_info.vector_observations
state_size = states.shape[1]
agent = DDPGAgent(state_size, action_size, conf.random_seed,
conf.buffer_size, conf.batch_size, conf.gamma,
conf.tau, conf.lr_actor, conf.lr_critic,
conf.weight_decay, conf.sigma,
conf.actor_nn_size, conf.critic_nn_size,
conf.batch_norm, conf.clip_grad_norm)
agent.load_weights(
actor_weights_file=checkpoint_path + "checkpoint_actor.pth",
critic_weights_file=checkpoint_path + "checkpoint_critic.pth")
demo(agent, env, num_episodes=100)
finally:
env.close()
else:
raise (ValueError, f"Unknown command {command}")
# State space information
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))
print('The state for the first agent looks like:', states[0])
print('========================================')
# ### Training the DDPG agent to solve the environment ### #
# In the following the training takes place.
# When training the environment, setting `train_mode=True` accelerates the
# simulation environment so that training can be done much quicker.
# Make a new agent from the DDPGAgent class in 'ddpg_agent.py'
agent = DDPGAgent(state_size=state_size,
action_size=action_size,
num_agents=num_agents,
random_seed=1) # , seed=random.randint(1,100000000)
# Print information about the network architecture
print('====================')
print('Actor-Critic network architecture')
print(agent.actor_local)
print(agent.critic_local)
print('====================')
# Set up a function that takes in information for training the agent for n_episodes.
def trainDDPG(n_episodes=1000, print_every=10):
'''Deep Deterministic Policy Gradient (DDPG) agent that can be trained for a set amount of episodes.
Keyword Arguments:
def __init__(self, random_seed, num_agents, state_size, action_size):
self.agents = [DDPGAgent(state_size,action_size,random_seed) for x in range(num_agents)]
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))
print('The state for the first agent looks like:', states[0])
for config in generate_grid_config():
agent = DDPGAgent(state_size,
action_size,
ddpg_config=config,
num_agents=num_agents)
agent.ddpg_actor_local.load_state_dict(
torch.load('ddpg_weights/actor_{}.pth'.format(config)))
agent.ddpg_critic_local.load_state_dict(
torch.load('ddpg_weights/critic_{}.pth'.format(config)))
evaluate_current_weights(env, agent, brain_name, num_agents, 5)
# torch.save(agent.actor_critic.state_dict(), 'weights/{}.pth'.format(agent.ddpg_config))
# agent.actor_critic.load_state_dict(torch.load('weights/{}.pth'.format(agent.ddpg_config)))
# test_agent(env, agent, brain_name, num_agents)
MAX_ITERATIONS = 200
def save_rewards(rewards):
with open("reward_data.pickle", "wb") as handle:
pickle.dump(rewards, handle)
# with tf.Session() as sess:
env = gym.make(ENV_NAME)
# print(env.observation_space.shape)
# print(env.action_space.shape)
n_states = env.observation_space.shape[0]
n_actions = env.action_space.shape[0]
agent = DDPGAgent(env)
rewards = []
for episode in range(MAX_EPISODES):
state = env.reset()
total_reward = 0
for itr in range(MAX_ITERATIONS):
action = agent.getNoisyAction(state)
# print("##### ", action, "####")
state_, reward, done, _ = env.step(action[0])
agent.observe(state, action, reward, state_, done)
state = state_
total_reward += reward
if done:
break
import gym
from puckworld_env import PuckWorldEnv
from ddpg_agent import DDPGAgent
from utils import learning_curve
import numpy as np
env = PuckWorldEnv()
agent =DDPGAgent(env)
data = agent.learning(max_episode_num=200, display=True)
learning_curve(data, 2, 1, #title="DDPGAgent performance on PuckWorld with continuous action space",
x_name="episodes", y_name="rewards of episode")
def train_agent(episodes=100, model='DDPG', print_every=10):
if model.lower() == 'd4pg':
agent = D4PGAgent()
print('Use D4PG agent......\n')
else:
agent = DDPGAgent()
print('Use default DDPG agent......\n')
print('Batch size: ', BATCH_SIZE)
print('Actor learning rate: ', LR_ACTOR)
print('Critic learning rate: ', LR_CRITIC)
print('\n')
env = EnvWrapper(file_name='Reacher_Windows_x86_64\Reacher.exe',
train_mode=True)
scores = []
scores_window = deque(maxlen=100)
for ep in range(1, episodes + 1):
agent.reset()
agent.states = env.reset()
for s in range(agent.max_steps):
agent.actions = agent.act(add_noise=True)
agent.rewards, agent.next_states, agent.dones = env.step(
agent.actions)
agent.step()
agent.states = agent.next_states
scores.append(agent.scores.mean())
scores_window.append(agent.scores.mean())
if ep % print_every == 0:
print('Episode %d, avg score: %.2f' % (ep, agent.scores.mean()))
if np.mean(scores_window) >= 30:
print(
'\nEnvironment solved in {:d} episodes!\tAverage Score: {:.2f}'
.format(ep - 100, np.mean(scores_window)))
torch.save(agent.actor.state_dict(),
'checkpoints/reacher_%s_actor_checkpoint.pth' % model)
torch.save(agent.critic.state_dict(),
'checkpoints/reacher_%s_critic_checkpoint.pth' % model)
env.close()
return scores, agent
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))
print('The state for the first agent looks like:', states[0])
# Set device between cuda:0 and cpu
torch_device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Device =', torch_device)
memory = ReplayBuffer(action_size, memory_params['buffer_size'],
memory_params['batch_size'], memory_params['seed'], torch_device)
ddpg_agents = [DDPGAgent(state_size, action_size, memory, torch_device, params)
for _ in range(num_agents)]
ddpg_scores = train(300, 5000, ddpg_agents, ["model_ddpg_actor.pth", "model_ddpg_critic.pth"],
benchmark_score, rolling_n_episodes)
plot_scores(ddpg_scores, benchmark_score, rolling_n_episodes)
# Test
ddpg_agents = [DDPGAgent(state_size, action_size, memory, torch_device, params)
for _ in range(num_agents)]
for agent in ddpg_agents:
agent.load_weights(["model_ddpg_actor.pth", "model_ddpg_critic.pth"])
test(ddpg_agents)
# Set the minimum score the agent has to reach in order to solve this task
threshold = 0.5
max_episodes = 5000
max_t = 100000
threshold = 2.0
conseq_episodes = 5
print_every = 1
mode = False
if mode == True:
#train(args)
#exit()
agent_1 = DDPGAgent(state_size, action_size)
#agent_2 = DDPGAgent(state_size, action_size)
agent_2 = TD3Agent(state_size, action_size)
agent_1_path = 'results/ddgp_solo/00_best_model.checkpoint'
#agent_2_path = 'results/temp/new_ddpg_model.checkpoint'
agent_2_path = 'results/temp/new_td3_model.checkpoint'
agent = MAAC(state_size, action_size, agent_1, agent_2, False, True)
agent.load(agent_1_path, 0)
df = train_CollabAndCompete(env, brain_name, max_episodes, max_t, threshold, \
conseq_episodes, print_every, agent, agent_1_path, agent_2_path)
plot_minmax(df)
def main():
test_mode = len(sys.argv) >= 2 and sys.argv[1] == "test"
env = get_swimmer6_env()
max_action_val = env.action_space.high[0]
min_action_val = env.action_space.low[0]
agent = DDPGAgent(env.state_space_dim,
env.action_space_dim,
min_action_val,
max_action_val,
hidden_layer_size=512,
path_to_load=MODEL_PATH)
episode_rewards = []
episodes_count = 1000000
with open("avgs.log", "w") as avgs_file:
for episode_index in range(episodes_count):
try:
episode_reward = 0
state = env.reset()
current_reward = None
while True:
if test_mode:
action = agent.get_best_action(state)
else:
action = agent.get_action(state)
next_state, reward, done, _ = env.step(action)
if current_reward is None:
current_reward = reward
continue
reward_diff = reward - current_reward
current_reward = reward
if not test_mode:
agent.remember_step(
(state, action, next_state, reward_diff))
agent.learn()
agent.update_targets()
episode_reward += reward_diff
if done:
break
state = next_state
episode_rewards.append(episode_reward)
avg = np.mean(episode_rewards[-100:])
print(
f"Episode #{episode_index}, reward: {episode_reward}, avg: {avg}"
)
avgs_file.write(str(episode_reward) + "\n")
avgs_file.flush()
if episode_index % 10 == 0 and not test_mode:
agent.save(MODEL_PATH)
except KeyboardInterrupt:
if not test_mode:
agent.save(MODEL_PATH)
cmd = input("Input: ")
if cmd == "1":
view_render(env, agent)
except Exception as e:
print(e)
n_hid1 = 400
n_hid2 = 300
lr_alpha = 1e-4
lr_beta = 1e-3
gamma = 0.99
tau = 0.99
fname = 'lunarlandercontinuous_ngames' + str(n_games) + '_memsize' + str(mem_size) + '_batchsize' + str(batch_size) + '_nhid1' + str(n_hid1)\
+ '_nhid2' + str(n_hid2) + '_lralpha' + str(lr_alpha) + '_lrbeta' + str(lr_beta) + '_gamma' + str(gamma) +\
'_tau' + str(tau)
figure_file = 'plots/' + fname + '.png'
checkpoint_file = 'models/' + fname
agent = DDPGAgent(load_checkpoint, n_states, n_actions, checkpoint_file,
mem_size, batch_size, n_hid1, n_hid2, lr_alpha, lr_beta,
gamma, tau)
env = gym.make('LunarLanderContinuous-v2')
if not load_checkpoint:
scores = []
n_to_consider = 100 # number of previous score to consider in the avg
best_score = env.reward_range[0]
for i in range(n_games):
done = False
score = 0
agent.noise.reset()
obs = env.reset()
while not done:
action = agent.choose_action(obs)
obs_, reward, done, info = env.step(action)
def main(path=''):
""" show the environment controlled by the 20 smart agents
Args:
param1: (string) pathname for saved network weights
"""
env = UnityEnvironment(file_name='Reacher_Linux/Reacher.x86_64', no_graphics=False)
# get the default brain
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
# reset the environment
env_info = env.reset(train_mode=False)[brain_name]
states = env_info.vector_observations
# number of agents
num_agents = len(env_info.agents)
print('Number of agents:', num_agents)
config = Config()
config.n_agents = num_agents
config.gamma = 0.99
config.state_dim = states.shape[1]
config.action_dim = brain.vector_action_space_size
config.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
config.seed = 42
config.leak = 0.001
config.tau = 1e-3
config.hdl1 = 256
config.hdl2 = 128
config.hdl3 = 128
config.lr_actor = 0.001
config.lr_critic = 0.001
config.batch_size = 1024
config.weight_decay = 0.99
config.memory_capacity = int(1e6)
agent = DDPGAgent(config)
agent.actor_local.load_state_dict(torch.load(path + 'checkpoint_actor.pth'))
agent.critic_local.load_state_dict(torch.load(path + 'checkpoint_critic.pth'))
for _ in range(3):
episode_reward = []
scores = np.zeros(num_agents)
env_info = env.reset(train_mode=False)[brain_name]
states = env_info.vector_observations
agent.reset_noise()
total_steps = 0
while True:
total_steps += 1
actions = agent.act(states, 0, False)
env_info = env.step(actions)[brain_name]
next_states = env_info.vector_observations
reward = env_info.rewards
done = np.array(env_info.local_done)
episode_reward.append(np.mean(reward))
scores += reward
states = next_states
if np.any(done):
print("total steps", total_steps)
print(sum(episode_reward))
print('average: ', np.mean(scores))
print('min: ', np.min(np.array(episode_reward)))
print('max: ', np.max(np.array(episode_reward)))
break
def main():
# Instanciate specified environment.
env = fe.FlockingEnv(size1, dynamic="first")
# Get environment specs
num_states = (size1 + 1) * dim * 2
num_actions = size1 * dim
# Print specs
print("Number of states: %d" % num_states)
print("Number of actions: %d" % num_actions)
print("-----------------------------------------")
# Instanciate reinforcement learning agent which contains Actor/Critic DNN.
#agents =[]
#for i in range(0,size):
agent = DDPGAgent(ob_shape=num_states, ac_shape=dim)
# agents.append(agent)
# Exploration noise generator which uses Ornstein-Uhlenbeck process.
noise = OUNoise(1)
for i in range(episodes_num):
print("--------Episode %d--------" % i)
reward_per_episode = 0
observation = env.reset_mul()
#observation = env.reset_full()
for j in range(steps_limit):
if is_movie_on: env.render()
# Select action off-policy
state = observation
action = np.zeros((size1, dim), dtype=np.float32)
# get individual ob states here
for k in range(0, size1):
ac = agent.feed_forward_actor(
np.reshape(state[k], [1, num_states]))
# print(noise.generate())
if i % 2 == 0:
action[k][0] = ac[0][0] + noise.generate()
action[k][1] = ac[0][1] + noise.generate()
else:
action[k][0] = ac[0][0] + noise.generate()
action[k][1] = ac[0][1] + noise.generate()
'''
action = agent.feed_forward_actor(np.reshape(state, [1, num_states]))
action = np.reshape(action, [size1,dim])
for k in range(0, size1):
if i % 2 == 0:
action[k][0] += noise.generate()
action[k][1] += noise.generate()
'''
# Throw action to environment
observation, reward, done, info = env.step_mul(action)
#observation, reward, done, info = env.step_full(action)
for k in range(0, size1):
agent.add_experience(np.reshape(state[k],
[num_states]), action[k],
np.reshape(observation[k], [num_states]),
reward[k], done)
#action=np.reshape(action,[num_actions])
#agent.add_experience(np.reshape(state, [num_states]), action,
# np.reshape(observation, [ num_states]), reward, done)
# Train actor/critic network
if len(agent.replay_buffer) > MINI_BATCH_SIZE: agent.train()
reward_per_episode = reward.sum()
if j % 100 == 0:
print(j, "step finished. reward=", reward_per_episode, "info=",
info)
# print("action=",action,"observation=",observation)
if (done or j == steps_limit - 1):
print("Steps count: %d" % j)
print("Total reward: %d" % reward_per_episode)
env.render()
#noise.reset()
with open("reward_log.csv", "a") as f:
f.write("%d,%f\n" % (i, reward_per_episode))
break