def play_func(params, net, cuda, exp_queue):
env = make(params.env_name)
env = wrap_dqn(env)
env.seed(common.SEED)
device = torch.device("cuda" if cuda else "cpu")
selector = EpsilonGreedyActionSelector(epsilon=params.epsilon_start)
epsilon_tracker = common.EpsilonTracker(selector, params)
agent = DQNAgent(net, selector, device=device)
exp_source = ExperienceSourceFirstLast(env, agent, gamma=params.gamma)
for frame_idx, exp in enumerate(exp_source):
epsilon_tracker.frame(frame_idx / BATCH_MUL)
exp_queue.put(exp)
for reward, steps in exp_source.pop_rewards_steps():
exp_queue.put(EpisodeEnded(reward, steps, selector.epsilon))
params = common.HYPERPARAMS["pong"]
parser = ArgumentParser()
parser.add_argument("--cuda",
default=True,
action="store_true",
help="Enable cuda")
parser.add_argument("-n",
type=int,
default=DEFAULT_N_STEPS,
help="steps to do on Bellman unroll")
args = parser.parse_args()
device = device("cuda" if args.cuda else "cpu")
env = make(params.env_name)
env = wrap_dqn(env)
env.seed(123)
net = dqn_model.DQN(env.observation_space.shape,
env.action_space.n).to(device)
tgt_net = TargetNet(net)
selector = EpsilonGreedyActionSelector(epsilon=params.epsilon_start)
epsilon_tracker = common.EpsilonTracker(selector, params)
agent = DQNAgent(net, selector, device=device)
exp_source = ExperienceSourceFirstLast(env,
agent,
gamma=params.gamma,
steps_count=args.n)
buffer = ExperienceReplayBuffer(exp_source, buffer_size=params.replay_size)
optimizer = Adam(net.parameters(), lr=params.learning_rate)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='n-step noisy DQN')
parser.add_argument('-n',
default=1,
type=int,
help='Enter the number of steps to unroll bellman eq')
args = parser.parse_args()
print('Starting...')
params = HYPERPARAMS['pong']
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('Running on Device {}'.format(device))
writer = writer = SummaryWriter(comment="-" + params['run_name'] +
"-%d-step noisy-net" % args.n)
env = gym.make(params['env_name'])
env = wrappers.wrap_dqn(env)
# print(env.observation_space.shape, env.action_space.n)
net = NoisyDQN(env.observation_space.shape, env.action_space.n).to(device)
target_net = TargetNet(net)
agent = DQNAgent(net, ArgmaxActionSelector(), device)
experience_source = ExperienceSourceFirstLast(env,
agent,
params['gamma'],
steps_count=args.n)
buffer = ExperienceReplayBuffer(experience_source,
buffer_size=params['replay_size'])
optimizer = optim.Adam(net.parameters(), lr=params['learning_rate'])
frame_idx = 0
