def __init__(self, model, env, memory, max_steps, max_episodes,
epsilon_start, epsilon_final, epsilon_decay, start_learning,
batch_size, save_update_freq, exploration_method, output_dir,
players, player_num, config_dir, map_file, unit_file,
env_output_dir, pnames, debug):
self.model = model
self.env = env
self.memory = memory
self.max_steps = max_steps
self.max_episodes = max_episodes
self.epsilon_start = epsilon_start
self.epsilon_final = epsilon_final
self.epsilon_decay = epsilon_decay
self.start_learning = start_learning
self.batch_size = batch_size
self.save_update_freq = save_update_freq
self.output_dir = output_dir
self.action_table = build_action_table(env.num_groups, env.num_nodes)
self.players = players
self.player_num = player_num
self.config_dir = config_dir
self.map_file = map_file
self.unit_file = unit_file
self.env_output_dir = env_output_dir
self.pnames = pnames
self.debug = debug
self.exploration_method = exploration_method
self.nodes_array = []
for i in range(1, self.env.num_nodes + 1):
self.nodes_array.append(i)
self._create_opponent_pool()
self.opp_num = 0
self.episode_cnt = 0
self.opp_save_freq = 10 # How many games before saving oppoent
self.opp_choose_freq = 0.2 # How often [0,1] to sample from the opponent pool
def __init__(self, observation_space, action_space, action_bins,
target_update_freq, learning_rate, gamma, hidden_dim,
td_target, device, exploration_method):
super().__init__()
self.observation_space = observation_space
self.action_space = action_space
self.action_bins = action_bins
self.gamma = gamma
self.exploration_method = exploration_method
self.policy_network = BranchingQNetwork(observation_space,
action_space, action_bins,
hidden_dim, exploration_method)
self.target_network = BranchingQNetwork(observation_space,
action_space, action_bins,
hidden_dim, exploration_method)
self.target_network.load_state_dict(self.policy_network.state_dict())
self.optim = optim.Adam(self.policy_network.parameters(),
lr=learning_rate)
self.policy_network.to(device)
self.target_network.to(device)
self.device = device
self.target_update_freq = target_update_freq
self.update_counter = 0
self.td_target = td_target
# TODO: Not hardcode the values for num groups and num nodes
self.action_table = build_action_table(12, 11)
def __init__(self,
observation_space,
action_space,
action_bins,
target_update_freq,
learning_rate,
gamma,
hidden_dim,
td_target,
device,
exploration_method,
architecture="Double",
isPer=False,
nSteps=3):
super().__init__()
self.observation_space = observation_space
self.isPer = isPer
self.action_space = action_space
self.action_bins = action_bins
self.gamma = gamma
self.exploration_method = exploration_method
self.architecture = architecture
self.player_helper = PlayerHelper(7, 1, "../config/DemoMap.json")
self.policy_network = BranchingQNetwork(observation_space,
action_space, action_bins,
hidden_dim, exploration_method,
architecture)
if architecture != "Base":
self.target_network = BranchingQNetwork(observation_space,
action_space, action_bins,
hidden_dim,
exploration_method,
architecture)
self.target_network.load_state_dict(
self.policy_network.state_dict())
self.target_network.to(device)
self.optim = optim.Adam(self.policy_network.parameters(),
lr=learning_rate)
self.action_choices = build_action_table()
self.policy_network.to(device)
self.device = device
self.target_update_freq = target_update_freq
self.update_counter = 0
self.td_target = td_target
self.nSteps = nSteps
self.nBuffer = []
def __init__(self, model,
env,
memory,
max_steps,
max_episodes,
epsilon_start,
epsilon_final,
epsilon_decay,
start_learning,
batch_size,
save_update_freq,
exploration_method,
output_dir,
players,
player_num,
config_dir,
map_file,
unit_file,
env_output_dir,
pnames,
debug,
renderer,
isNSteps):
self.model = model
self.env = env
self.memory = memory
self.max_steps = max_steps
self.max_episodes = max_episodes
self.epsilon_start = epsilon_start
self.epsilon_final = epsilon_final
self.epsilon_decay = epsilon_decay
self.start_learning = start_learning
self.batch_size = batch_size
self.save_update_freq = save_update_freq
self.output_dir = output_dir
self.action_table = build_action_table(env.num_groups, env.num_nodes)
self.player_helper = PlayerHelper(7,1, "../config/DemoMap.json")
self.players = players
self.player_num = player_num
self.config_dir = config_dir
self.map_file = map_file
self.unit_file = unit_file
self.env_output_dir = env_output_dir
self.pnames = pnames
self.player_name="random_actions"
self.debug = debug
self.exploration_method = exploration_method
self.nodes_array = []
self.renderer = renderer
if isNSteps > 0:
self.isNSteps = True
else:
self.isNSteps = False
for i in range(1, self.env.num_nodes + 1):
self.nodes_array.append(i)
)
bdqn_player_num = 1
"""Load Pre-Saved Model"""
#bdqn_player.load_state_dict(torch.load(
# './runs/Michael_Local/model_state_dict_last'))
#bdqn_player.load_state_dict(torch.load(
# './agents/bd3qn/runs/Newton-train-2-7-21-res/model_state_dict_best'))
bdqn_player.load_state_dict(torch.load(
'./runs/Michael_Local_2/model_state_dict_last'))
bdqn_player.to(device)
players[0] = rand_player
players[1] = bdqn_player
names[0] = rand_player.__class__.__name__
names[1] = bdqn_player.__class__.__name__
action_table = utils.build_action_table(env.num_groups, env.num_nodes)
to_render = True
r = Renderer(map_name,frame_collection=True)
gif_frames = []
# Play
total_wins = 0
game_played = 0
winrate = 0
for episode in range(1):
state = env.reset(
players=players,
config_dir=config.env_config,
map_file=config.map_file,
unit_file=config.unit_file,