parser.add_argument('--batch_size', type=int, default=4, metavar='N')
parser.add_argument('--num_steps', type=int, default=1000001, metavar='N')
parser.add_argument('--hidden_size', type=int, default=512, metavar='N')
parser.add_argument('--updates_per_step', type=int, default=1, metavar='N')
parser.add_argument('--start_steps', type=int, default=10000, metavar='N')
parser.add_argument('--target_update_interval', type=int, default=1, metavar='N')
parser.add_argument('--replay_size', type=int, default=1000000, metavar='N')
args = parser.parse_args()
args.cuda =True if torch.cuda.is_available() else False
env = h_env.HockeyEnv(mode=h_env.HockeyEnv.NORMAL)
# Agent
agent = SAC(env.observation_space.shape[0], env.action_space, args)
agent.load_model('full_player_models/sac_actor_hockey_11200_batch_size-4_gamma-0.95_tau-0.005_lr-0.0003_alpha-0.2_tuning-True_hidden_size-256_updatesStep-1_startSteps-10000_targetIntervall-1_replaysize-1000000','full_player_models/sac_critic_hockey_11200_batch_size-4_gamma-0.95_tau-0.005_lr-0.0003_alpha-0.2_tuning-True_hidden_size-256_updatesStep-1_startSteps-10000_targetIntervall-1_replaysize-1000000')
# opponent = copy.deepcopy(agent)
basic_strong = h_env.BasicOpponent(weak=False)
time_ = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
#Tesnorboard
writer = SummaryWriter(f"strongplay-runs/ERE{time_}_batch_size-{args.batch_size}_gamma-{args.gamma}_tau-{args.tau}_lr-{args.lr}_alpha-{args.alpha}_tuning-{args.automatic_entropy_tuning}_hidden_size-{args.hidden_size}_updatesStep-{args.updates_per_step}_startSteps-{args.start_steps}_targetIntervall-{args.target_update_interval}_replaysize-{args.replay_size}")
# Memory
memory = ERE_PrioritizedReplay(args.replay_size)
# memory = ReplayMemory(args.replay_size,args.seed)
# Training Loop
total_numsteps = 0
updates = 0
args = parser.parse_args()
args.cuda = True if torch.cuda.is_available() else False
# Environment
# env = NormalizedActions(gym.make(args.env_name))
env = gym.make(args.env_name)
env.seed(args.seed)
env.action_space.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# Agent
agent = SAC(env.observation_space.shape[0], env.action_space, args)
#Tesnorboard
writer = SummaryWriter('runs/{}_SAC_{}_{}_{}'.format(
datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"), args.env_name,
args.policy, "autotune" if args.automatic_entropy_tuning else ""))
# Memory
memory = PrioritizedReplay(args.replay_size)
# Training Loop
total_numsteps = 0
updates = 0
for i_episode in itertools.count(1):
episode_reward = 0
parser.add_argument('--batch_size', type=int, default=8, metavar='N')
parser.add_argument('--num_steps', type=int, default=1000001, metavar='N')
parser.add_argument('--hidden_size', type=int, default=512, metavar='N')
parser.add_argument('--updates_per_step', type=int, default=1, metavar='N')
parser.add_argument('--start_steps', type=int, default=10000, metavar='N')
parser.add_argument('--target_update_interval', type=int, default=1, metavar='N')
parser.add_argument('--replay_size', type=int, default=1000000, metavar='N')
args = parser.parse_args()
args.cuda =True if torch.cuda.is_available() else False
env = h_env.HockeyEnv(mode=h_env.HockeyEnv.NORMAL)
# Agent
agent = SAC(env.observation_space.shape[0], env.action_space, args)
actor = "full_player_models/sac_actor_hockey_11200_batch_size-4_gamma-0.95_tau-0.005_lr-0.0003_alpha-0.2_tuning-True_hidden_size-256_updatesStep-1_startSteps-10000_targetIntervall-1_replaysize-1000000"
critic = "full_player_models/sac_critic_hockey_11200_batch_size-4_gamma-0.95_tau-0.005_lr-0.0003_alpha-0.2_tuning-True_hidden_size-256_updatesStep-1_startSteps-10000_targetIntervall-1_replaysize-1000000"
agent.load_model(actor,critic)
# opponent = copy.deepcopy(agent)
opponent = h_env.BasicOpponent(weak=False)
time_ = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
#Tesnorboard
# Memory
memory = PrioritizedReplay(args.replay_size)
# memory = ReplayMemory(args.replay_size,args.seed)
# Training Loop
total_numsteps = 0
type=int,
default=1,
metavar='N')
parser.add_argument('--replay_size', type=int, default=1000000, metavar='N')
args = parser.parse_args()
args.cuda = True if torch.cuda.is_available() else False
env = h_env.HockeyEnv(mode=h_env.HockeyEnv.NORMAL)
# Agent
root = 'finals/'
runs = sorted(os.listdir(root))
runs = [r + '/' for r in runs]
agent = SAC(env.observation_space.shape[0], env.action_space, args)
opponent = SAC(env.observation_space.shape[0], env.action_space, args)
player1 = 1
player2 = 3
basic1 = False
basic2 = False
# print(f"{runs[player1]} vs {runs[player2]}")
print(f"{runs[player1]} vs {runs[player2]}")
models1 = sorted(os.listdir(root + runs[player1]))
actor = root + runs[player1] + models1[0]
critic = root + runs[player1] + models1[1]
target = root + runs[player1] + models1[2] if len(models1) == 3 else None
models2 = sorted(os.listdir(root + runs[player2]))
o_actor = root + runs[player2] + models2[0]