def train():
# Set random seeds for reproducability
np.random.seed(train_params.RANDOM_SEED)
random.seed(train_params.RANDOM_SEED)
tf.random.set_seed(train_params.RANDOM_SEED)
# Initialise prioritised experience replay memory
PER_memory = PrioritizedReplayBuffer(train_params.REPLAY_MEM_SIZE,
train_params.PRIORITY_ALPHA)
# Initialise Gaussian noise generator
gaussian_noise = GaussianNoiseGenerator(train_params.ACTION_DIMS,
train_params.ACTION_BOUND_LOW,
train_params.ACTION_BOUND_HIGH,
train_params.NOISE_SCALE)
# Create threads for learner process and agent processes
threads = []
# Create threading events for communication and synchronisation between the learner and agent threads
run_agent_event = threading.Event()
stop_agent_event = threading.Event()
# with tf.device('/device:GPU:0'):
# Initialise learner
learner = Learner(PER_memory, run_agent_event, stop_agent_event)
# Build learner networks
learner.build_network()
# Initialise variables (either from ckpt file if given, or from random)
learner.initialise_vars()
# Get learner policy (actor) network params - agent needs these to copy latest policy params periodically
learner_policy_params = learner.actor_net.trainable_variables # TODO : consider batch later
threads.append(threading.Thread(target=learner.run))
for n_agent in range(train_params.NUM_AGENTS):
# Initialise agent
agent = Agent(train_params.ENV, train_params.RANDOM_SEED,
learner_policy_params, n_agent)
# Build network
agent.build_network(training=True)
agent.build_summaries(train_params.LOG_DIR +
('/train/agent_%02d' % n_agent))
threads.append(
threading.Thread(target=agent.run,
args=(PER_memory, gaussian_noise, run_agent_event,
stop_agent_event)))
for t in threads:
t.start()
for t in threads:
t.join()
def train():
tf.reset_default_graph()
# Set random seeds for reproducability
np.random.seed(train_params.RANDOM_SEED)
random.seed(train_params.RANDOM_SEED)
tf.set_random_seed(train_params.RANDOM_SEED)
# Initialise prioritised experience replay memory
PER_memory = PrioritizedReplayBuffer(train_params.REPLAY_MEM_SIZE,
train_params.PRIORITY_ALPHA)
# Initialise Gaussian noise generator
gaussian_noise = GaussianNoiseGenerator(train_params.ACTION_DIMS,
train_params.ACTION_BOUND_LOW,
train_params.ACTION_BOUND_HIGH,
train_params.NOISE_SCALE)
# Create session
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# Create threads for learner process and agent processes
threads = []
# Create threading events for communication and synchronisation between the learner and agent threads
run_agent_event = threading.Event()
stop_agent_event = threading.Event()
# with tf.device('/device:GPU:0'):
# Initialise learner
learner = Learner(sess, PER_memory, run_agent_event, stop_agent_event)
# Build learner networks
learner.build_network()
# Build ops to update target networks
learner.build_update_ops()
# Initialise variables (either from ckpt file if given, or from random)
learner.initialise_vars()
# Get learner policy (actor) network params - agent needs these to copy latest policy params periodically
learner_policy_params = learner.actor_net.network_params + learner.actor_net.bn_params
threads.append(threading.Thread(target=learner.run))
for n_agent in range(train_params.NUM_AGENTS):
# Initialise agent
agent = Agent(sess, train_params.ENV, train_params.RANDOM_SEED,
n_agent)
# Build network
agent.build_network(training=True)
# Build op to periodically update agent network params from learner network
agent.build_update_op(learner_policy_params)
# Create Tensorboard summaries to save episode rewards
if train_params.LOG_DIR is not None:
agent.build_summaries(train_params.LOG_DIR +
('/agent_%02d' % n_agent))
threads.append(
threading.Thread(target=agent.run,
args=(PER_memory, gaussian_noise, run_agent_event,
stop_agent_event)))
for t in threads:
t.start()
for t in threads:
t.join()
sess.close()
