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, env):
self.args = args
self.evolver = utils_ne.SSNE(self.args)
# self.replay_buffer = replay_memory.ReplayMemory(args.buffer_size)
self.pop = []
for _ in range(args.pop_size):
self.pop.append(ddpg.Actor(args))
for actor in self.pop: actor.eval()
# self.workers = [Worker.remote(args) for _ in range(self.args.pop_size+1)]
# args.is_cuda = True; args.is_memory_cuda = True
self.rl_agent = ddpg.DDPG(args)
# self.rl_agent.share_memory()
self.ounoise = ddpg.OUNoise(args.action_dim)
# self.replay_queue = mp.Manager().Queue() # mp.Manager().list()
# self.replay_queue = mp.Queue()
# self.replay_memory = mp.Manager().list()
# self.replay_memory = mp.Array()
# self.replay_memory = mp.Queue()
self.workers = self.pop.append(self.rl_agent.actor)
# for key in range(self.args.pop_size):
# self.replay_memory[key] = replay_memory.ReplayMemory(self.args.buffer_size)
# self.learner = LearnerThread(self.replay_memory, self.rl_agent)
# self.learner.start()
# Stats
# self.timers = {
# k: TimerStat()
# for k in [
# "put_weights", "get_samples", "sample_processing",
# "replay_processing", "update_priorities", "train", "sample"
# ]
# }
self.num_games = 0; self.num_frames = 0; self.gen_frames = 0; self.len_replay = 0