def __init__(self, args, env):
self.args = args
self.env = env
self.evolver = utils_ne.SSNE(self.args)
self.best_r = 0
self.best_state = []
#Init population
self.pop = []
for _ in range(args.pop_size):
self.pop.append(ddpg.Actor(args))
#Turn off gradients and put in eval mode
for actor in self.pop:
actor.eval()
#Init RL Agent
self.rl_agent = ddpg.DDPG(args)
self.replay_buffer = replay_memory.ReplayMemory(args.buffer_size)
self.ounoise = ddpg.OUNoise(args.action_dim)
#Trackers
self.num_games = 0
self.num_frames = 0
self.gen_frames = None
def __init__(self, args: Parameters, env):
self.args = args; self.env = env
# Init population
self.pop = []
self.buffers = []
for _ in range(args.pop_size):
self.pop.append(ddpg.GeneticAgent(args))
# Init RL Agent
self.rl_agent = ddpg.DDPG(args)
if args.per:
self.replay_buffer = replay_memory.PrioritizedReplayMemory(args.buffer_size, args.device,
beta_frames=self.args.num_frames)
else:
self.replay_buffer = replay_memory.ReplayMemory(args.buffer_size, args.device)
self.ounoise = ddpg.OUNoise(args.action_dim)
self.evolver = utils_ne.SSNE(self.args, self.rl_agent.critic, self.evaluate)
# Population novelty
self.ns_r = 1.0
self.ns_delta = 0.1
self.best_train_reward = 0.0
self.time_since_improv = 0
self.step = 1
# Trackers
self.num_games = 0; self.num_frames = 0; self.iterations = 0; self.gen_frames = None
def __init__(self, args: Parameters):
self.args = args
self.actor = Actor(args)
self.actor_optim = Adam(self.actor.parameters(), lr=1e-3)
self.buffer = replay_memory.ReplayMemory(self.args.individual_bs,
args.device)
self.loss = nn.MSELoss()
def __init__(self, args, env):
self.args = args
self.env = env
#Create the Agent
self.agent = magent.MetaAgent(args.state_dim, args.action_dim,
args.num_subs, args.master_lr,
args.sub_lr)
#Init Replay Buffer
self.replay_buffer = replay_memory.ReplayMemory(args.buffer_size)
def __init__(self, args):
self.args = args
self.buffer = replay_memory.ReplayMemory(args.individual_bs,
args.device)
self.actor = Actor(args, init=True)
self.actor_target = Actor(args, init=True)
self.actor_optim = Adam(self.actor.parameters(), lr=0.5e-4)
self.critic = Critic(args)
self.critic_target = Critic(args)
self.critic_optim = Adam(self.critic.parameters(), lr=0.5e-3)
self.gamma = args.gamma
self.tau = self.args.tau
self.loss = nn.MSELoss()
hard_update(self.actor_target,
self.actor) # Make sure target is with the same weight
hard_update(self.critic_target, self.critic)