def main(game, _seed, _run):
torch.manual_seed(_seed)
game = lower_under_to_upper(game) + 'NoFrameskip-v4'
env = gym.make(game)
env = wrap_deepmind(env)
input_space = env.observation_space
num_actions = env.action_space.n
agent_params = DeepQAgentParams()
add_params(params=agent_params, prefix='agent')
add_params(params=agent_params.optimizer_params, prefix='opt')
add_epsilon_params(params=agent_params)
agent_params.obs_filter = AtariObservationFilter()
input_space = agent_params.obs_filter.output_space(input_space)
agent_params.sacred_run = _run
agent_params.env = env
agent_params.mode = 'train'
online_q_net = build_net(input_shape=input_space.shape,
num_actions=num_actions)
target_q_net = build_net(input_shape=input_space.shape,
num_actions=num_actions)
agent_params.online_q_net = online_q_net
agent_params.target_q_net = target_q_net
agent = agent_params.make_agent()
agent.run()
def main(_seed, _run, env):
torch.manual_seed(_seed)
train_env, test_env = build_env(**env['train']), build_env(**env['test'])
input_shape = train_env.observation_space.shape
num_actions = test_env.action_space.n
agent_params = DeepQAgentParams()
add_params(params=agent_params, prefix='agent')
add_params(params=agent_params.optimizer_params, prefix='opt')
add_epsilon_params(params=agent_params)
agent_params.sacred_run = _run
agent_params.train_env = train_env
agent_params.test_envs.append(test_env)
online_q_net = build_net(input_shape=input_shape, num_actions=num_actions)
target_q_net = build_net(input_shape=input_shape, num_actions=num_actions)
agent_params.online_q_net = online_q_net
agent_params.target_q_net = target_q_net
agent_params.obs_filter = batch_fn
agent = agent_params.make_agent()
agent.run()
def main(_seed, _run, env):
torch.manual_seed(_seed)
train_envs = build_envs(**env['train'])
train_env = SampleEnv(train_envs)
test_envs = build_envs(**env['test'])
input_shape = train_env.observation_space.shape
num_actions = train_env.action_space.n
agent_params = ActorCriticAgentParams()
add_params(params=agent_params, prefix='agent')
add_params(params=agent_params.optimizer_params, prefix='opt')
agent_params.sacred_run = _run
agent_params.train_env = train_env
agent_params.test_envs = test_envs
policy_value_net = build_net(input_shape=input_shape,
num_actions=num_actions)
agent_params.policy_value_net = policy_value_net
agent = agent_params.make_agent()
agent.run()
def main(_seed, _run):
torch.manual_seed(_seed)
env = build_env()
input_shape = env.observation_space.shape
num_actions = env.action_space.n
agent_params = PolicyGradientsAgentParams()
add_params(params=agent_params, prefix='agent')
add_params(params=agent_params.optimizer_params, prefix='opt')
agent_params.sacred_run = _run
agent_params.env = env
agent_params.mode = 'train'
policy_net = build_net(input_shape=input_shape, num_actions=num_actions)
agent_params.policy_net = policy_net
agent = agent_params.make_agent()
agent.run()
def main(_seed, _run):
torch.manual_seed(_seed)
env = gym.make('CartPole-v0')
input_shape = env.observation_space.shape
num_actions = env.action_space.n
agent_params = PolicyGradientsAgentParams()
add_params(params=agent_params, prefix='agent')
add_params(params=agent_params.optimizer_params, prefix='opt')
agent_params.sacred_run = _run
agent_params.env = env
agent_params.mode = 'train'
agent_params.reward_filter = RewardRescaleFilter(200.)
policy_net = build_net(input_shape=input_shape, num_actions=num_actions)
agent_params.policy_net = policy_net
agent = agent_params.make_agent()
agent.run()
def main(_seed, _run, env):
torch.manual_seed(_seed)
train_envs, (kg_entities, _, num_node_feats,
num_edge_feats) = build_envs(**env['train'])
num_entities = len(kg_entities)
train_env = SampleEnv(train_envs)
test_envs, _ = build_envs(**env['test'])
input_shape = train_env.observation_space.shape
num_actions = train_env.action_space.n
agent_params = DeepQAgentParams()
add_params(params=agent_params, prefix='agent')
add_params(params=agent_params.optimizer_params, prefix='opt')
add_epsilon_params(params=agent_params)
add_stopping_params(params=agent_params)
agent_params.sacred_run = _run
agent_params.train_env = train_env
agent_params.test_envs = test_envs
agent_params.obs_filter = GraphEnv.batch_observations
online_q_net = build_net(input_shape=input_shape,
num_actions=num_actions,
num_entities=num_entities,
num_node_feats=num_node_feats,
num_edge_feats=num_edge_feats)
target_q_net = build_net(input_shape=input_shape,
num_actions=num_actions,
num_entities=num_entities,
num_node_feats=num_node_feats,
num_edge_feats=num_edge_feats)
agent_params.online_q_net = online_q_net
agent_params.target_q_net = target_q_net
agent = agent_params.make_agent()
agent.run()
def main(_seed, _run):
torch.manual_seed(_seed)
env = build_env()
input_shape = env.observation_space.shape
num_actions = env.action_space.n
agent_params = DeepQAgentParams()
add_params(params=agent_params, prefix='agent')
add_params(params=agent_params.optimizer_params, prefix='opt')
add_epsilon_params(params=agent_params)
agent_params.sacred_run = _run
agent_params.env = env
agent_params.mode = 'train'
online_q_net = build_net(input_shape=input_shape, num_actions=num_actions)
target_q_net = build_net(input_shape=input_shape, num_actions=num_actions)
agent_params.online_q_net = online_q_net
agent_params.target_q_net = target_q_net
agent = agent_params.make_agent()
agent.run()
