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train_atari_random_new_nn.py
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import os
os.environ['SDL_AUDIODRIVER'] = 'dsp'
import sys
import gym
import random
import numpy as np
import ray
from ray import tune
from ray.rllib.models import ModelCatalog
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.models.tf.misc import normc_initializer
from ray.tune.registry import register_env
from ray.rllib.utils import try_import_tf
from pettingzooenv import PettingZooEnv
from pettingzoo.atari import boxing_v0, combat_tank_v0, joust_v0, surround_v0, space_invaders_v0
from supersuit.aec_wrappers import clip_reward, sticky_actions, resize
from supersuit.aec_wrappers import frame_skip, frame_stack, agent_indicator
from random_policy import RandomPolicy
#from cyclic_reward_wrapper import cyclic_reward_wrapper
tf1, tf, tfv = try_import_tf()
class AtariModel(TFModelV2):
def __init__(self, obs_space, action_space, num_outputs, model_config,
name="atari_model"):
super(AtariModel, self).__init__(obs_space, action_space, num_outputs, model_config,
name)
inputs = tf.keras.layers.Input(shape=(84,84,4), name='observations')
inputs2 = tf.keras.layers.Input(shape=(2,), name='agent_indicator')
# Convolutions on the frames on the screen
layer1 = tf.keras.layers.Conv2D(
32,
8,
strides=4,
activation="relu",
data_format='channels_last')(inputs)
layer2 = tf.keras.layers.Conv2D(
64,
4,
strides=2,
activation="relu",
data_format='channels_last')(layer1)
layer3 = tf.keras.layers.Conv2D(
64,
3,
strides=1,
activation="relu",
data_format='channels_last')(layer2)
layer4 = tf.keras.layers.Flatten()(layer3)
concat_layer = tf.keras.layers.Concatenate()([layer4, inputs2])
layer5 = tf.keras.layers.Dense(512, activation="relu")(concat_layer)
action = tf.keras.layers.Dense(
num_outputs,
activation="linear",
name="actions")(layer5)
value_out = tf.keras.layers.Dense(
1,
activation=None,
name="value_out",
kernel_initializer=normc_initializer(0.01))(layer5)
self.base_model = tf.keras.Model([inputs,inputs2], [action, value_out])
self.register_variables(self.base_model.variables)
def forward(self, input_dict, state, seq_lens):
print("============================================================")
print("============================================================")
print("============================================================")
print("============================================================")
print("============================================================")
print("input dict: {}".format(input_dict))
print("obs: {}".format(input_dict['obs']))
model_out, self._value_out = self.base_model([input_dict["obs"][:,:,:,0:4],input_dict["obs"][:,0,0,4:6]])
return model_out, state
def value_function(self):
return tf.reshape(self._value_out, [-1])
if __name__ == "__main__":
# RDQN - Rainbow DQN
# ADQN - Apex DQN
methods = ["ADQN", "PPO", "RDQN"]
assert len(sys.argv) == 3, "Input the learning method as the second argument"
env_name = sys.argv[1].lower()
method = sys.argv[2]
assert method in methods, "Method should be one of {}".format(methods)
if env_name=='boxing':
game_env = boxing_v0
elif env_name=='combat_tank':
game_env = combat_tank_v0
elif env_name=='joust':
game_env = joust_v0
elif env_name=='surround':
game_env = surround_v0
elif env_name=='space_invaders':
game_env = space_invaders_v0
else:
raise TypeError('{} environment not supported!'.format(env_name))
def env_creator(args):
env = game_env.env(obs_type='grayscale_image')
#env = clip_reward(env, lower_bound=-1, upper_bound=1)
#env = sticky_actions(env, repeat_action_probability=0.25)
env = resize(env, 84, 84)
#env = color_reduction(env, mode='full')
env = frame_stack(env, 4)
env = agent_indicator(env, type_only=False)
#env = frame_skip(env, 4)
return env
register_env(env_name, lambda config: PettingZooEnv(env_creator(config)))
test_env = PettingZooEnv(env_creator({}))
obs_space = test_env.observation_space
act_space = test_env.action_space
ModelCatalog.register_custom_model("AtariModel", AtariModel)
#ModelCatalog.register_custom_model("RandomPolicy", RandomPolicy)
def gen_policy(i):
config = {
"model": {
"custom_model": "AtariModel",
},
"gamma": 0.99,
}
return (None, obs_space, act_space, config)
policies = {
"policy_0": gen_policy(0),
"random": (RandomPolicy, obs_space, act_space, {"ignore_action_bounds": True}),
}
# for all methods
policy_ids = list(policies.keys())
def select_policy(agent_id):
if agent_id == "first_0":
return "policy_0"
else:
return "random"
if method == "A2C":
tune.run(
"A2C",
name="A2C",
stop={"episodes_total": 60000},
checkpoint_freq=10,
local_dir="~/ray_results_base/"+env_name,
config={
# Enviroment specific
"env": env_name,
# General
"log_level": "ERROR",
"num_gpus": 1,
"num_workers": 8,
"num_envs_per_worker": 8,
"compress_observations": False,
"sample_batch_size": 20,
"train_batch_size": 512,
"gamma": .99,
"lr_schedule": [[0, 0.0007],[20000000, 0.000000000001]],
# Method specific
"multiagent": {
"policies": policies,
"policy_mapping_fn": select_policy,
},
},
)
elif method == "ADQN":
# APEX-DQN
tune.run(
"APEX",
name="ADQN",
stop={"episodes_total": 600000},
checkpoint_freq=10,
local_dir="~/ray_results_base/"+env_name,
config={
# Enviroment specific
"env": env_name,
"double_q": False,
"dueling": False,
"num_atoms": 1,
"noisy": False,
"n_step": 3,
"lr": 0.0001,
"adam_epsilon": 0.00015,
"buffer_size": int(6e5),
"exploration_config": {
"final_epsilon": 0.01,
"epsilon_timesteps": 200000,
},
"prioritized_replay_alpha": 0.5,
"final_prioritized_replay_beta": 1.0,
"prioritized_replay_beta_annealing_timesteps": 2000000,
"num_gpus": 1,
"log_level": "INFO",
"num_workers": 8,
"num_envs_per_worker": 8,
"rollout_fragment_length": 20,
"train_batch_size": 512,
"target_network_update_freq": 50000,
"timesteps_per_iteration": 25000,
#"learning_starts": 80000,
"compress_observations": False,
"gamma": 0.99,
# Method specific
"multiagent": {
"policies_to_train":["policy_0"],
"policies": policies,
"policy_mapping_fn": select_policy,
},
},
)
elif method == "DQN":
# plain DQN
tune.run(
"DQN",
name="DQN",
stop={"episodes_total": 60000},
checkpoint_freq=10,
local_dir="~/ray_results_base/"+env_name,
config={
# Enviroment specific
"env": env_name,
# General
"log_level": "ERROR",
"num_gpus": 1,
"num_workers": 8,
"num_envs_per_worker": 8,
"learning_starts": 1000,
"buffer_size": int(1e5),
"compress_observations": True,
"sample_batch_size": 20,
"train_batch_size": 512,
"gamma": .99,
# Method specific
"dueling": False,
"double_q": False,
"multiagent": {
"policies": policies,
"policy_mapping_fn": (
lambda agent_id: policy_ids[0]),
},
},
)
elif method == "IMPALA":
tune.run(
"IMPALA",
name="IMPALA",
stop={"episodes_total": 60000},
checkpoint_freq=10,
local_dir="~/ray_results_base/"+env_name,
config={
# Enviroment specific
"env": env_name,
# General
"log_level": "ERROR",
"num_gpus": 1,
"num_workers": 8,
"num_envs_per_worker": 8,
"compress_observations": True,
"sample_batch_size": 20,
"train_batch_size": 512,
"gamma": .99,
"clip_rewards": True,
"lr_schedule": [[0, 0.0005],[20000000, 0.000000000001]],
# Method specific
"multiagent": {
"policies": policies,
"policy_mapping_fn": (
lambda agent_id: policy_ids[0]),
},
},
)
elif method == "PPO":
tune.run(
"PPO",
name="PPO",
stop={"episodes_total": 60000},
checkpoint_freq=10,
local_dir="~/ray_results_base/"+env_name,
config={
# Enviroment specific
"env": env_name,
# General
"log_level": "ERROR",
"num_gpus": 1,
"num_workers": 8,
"num_envs_per_worker": 8,
"compress_observations": False,
"gamma": .99,
"lambda": 0.95,
"kl_coeff": 0.5,
"clip_rewards": True,
"clip_param": 0.1,
"vf_clip_param": 10.0,
"entropy_coeff": 0.01,
"train_batch_size": 5000,
"sample_batch_size": 100,
"sgd_minibatch_size": 500,
"num_sgd_iter": 10,
"batch_mode": 'truncate_episodes',
"vf_share_layers": True,
# Method specific
"multiagent": {
"policies": policies,
"policy_mapping_fn": (
lambda agent_id: policy_ids[0]),
},
},
)
# pseudo-rainbow DQN
elif method == "RDQN":
tune.run(
"DQN",
name="RDQN",
stop={"episodes_total": 60000},
checkpoint_freq=10,
local_dir="~/ray_results_base/"+env_name,
config={
# Enviroment specific
"env": env_name,
# General
"log_level": "ERROR",
"num_gpus": 1,
"num_workers": 8,
"num_envs_per_worker": 8,
"learning_starts": 1000,
"buffer_size": int(1e5),
"compress_observations": True,
"sample_batch_size": 20,
"train_batch_size": 512,
"gamma": .99,
# Method specific
"num_atoms": 51,
"dueling": True,
"double_q": True,
"n_step": 2,
"batch_mode": "complete_episodes",
"prioritized_replay": True,
# # alternative 1
# "noisy": True,
# alternative 2
"parameter_noise": True,
# based on expected return
"v_min": 0,
"v_max": 1500,
"multiagent": {
"policies": policies,
"policy_mapping_fn": (
lambda agent_id: policy_ids[0]),
},
},
)