def test_can_unhook_tql_agenthook(RPSTask, tabular_q_learning_config_dict):
agent = build_TabularQ_Agent(RPSTask, tabular_q_learning_config_dict,
'TQL')
hook = AgentHook(agent)
retrieved_agent = AgentHook.unhook(hook)
compare_against_expected_retrieved_agent(agent, retrieved_agent, [])
def test_can_load_tql_from_agenthook(RPSTask, tabular_q_learning_config_dict):
agent = build_TabularQ_Agent(RPSTask, tabular_q_learning_config_dict,
'TQL')
save_path = '/tmp/test_save.agent'
hook = AgentHook(agent, save_path=save_path)
retrieved_agent = AgentHook.unhook(hook)
assert np.array_equal(agent.algorithm.Q_table,
retrieved_agent.algorithm.Q_table)
def test_can_load_ppo_from_agenthook_disabling_cuda(RPSTask, ppo_config_dict):
ppo_config_dict['use_cuda'] = True
agent = build_PPO_Agent(RPSTask, ppo_config_dict, 'PPO')
save_path = '/tmp/test_save.agent'
hook = AgentHook(agent, save_path=save_path)
retrieved_agent = AgentHook.unhook(hook, use_cuda=False)
model = retrieved_agent.algorithm.model
assert all(map(lambda param: not param.is_cuda, model.parameters()))
def test_can_unhook_ppo_agenthook_with_cuda(RPSTask, ppo_config_dict):
ppo_config_dict['use_cuda'] = True
agent = build_PPO_Agent(RPSTask, ppo_config_dict, 'PPO')
assert all(
map(lambda param: param.is_cuda, agent.algorithm.model.parameters()))
hook = AgentHook(agent)
retrieved_agent = AgentHook.unhook(hook)
compare_against_expected_retrieved_agent(agent, retrieved_agent,
[retrieved_agent.algorithm.model])
def test_can_load_ppo_from_agenthook_with_cuda(RPSTask, ppo_config_dict):
ppo_config_dict['use_cuda'] = True
agent = build_PPO_Agent(RPSTask, ppo_config_dict, 'PPO')
save_path = '/tmp/test_save.agent'
hook = AgentHook(agent, save_path=save_path)
assert not hasattr(hook, 'agent')
retrieved_agent = AgentHook.unhook(hook)
model_list = [retrieved_agent.algorithm.model]
assert_model_parameters_are_cuda_tensors(model_list)
def test_can_load_dqn_from_agenthook_with_cuda(RPSTask, dqn_config_dict):
dqn_config_dict['use_cuda'] = True
agent = build_DQN_Agent(RPSTask, dqn_config_dict, 'DQN')
save_path = '/tmp/test_save.agent'
hook = AgentHook(agent, save_path=save_path)
retrieved_agent = AgentHook.unhook(hook)
model_list = [
retrieved_agent.algorithm.model, retrieved_agent.algorithm.target_model
]
assert_model_parameters_are_cuda_tensors(model_list)
def test_can_unhook_dqn_agenthook_cuda(RPSTask, dqn_config_dict):
dqn_config_dict['use_cuda'] = True
agent = build_DQN_Agent(RPSTask, dqn_config_dict, 'DQN')
assert all(
map(lambda param: param.is_cuda, agent.algorithm.model.parameters()))
assert all(
map(lambda param: param.is_cuda,
agent.algorithm.target_model.parameters()))
hook = AgentHook(agent)
retrieved_agent = AgentHook.unhook(hook)
compare_against_expected_retrieved_agent(agent, retrieved_agent, [
retrieved_agent.algorithm.model, retrieved_agent.algorithm.target_model
])
def test_can_save_ppo_to_memory(RPSTask, ppo_config_dict):
agent = build_PPO_Agent(RPSTask, ppo_config_dict, 'PPO')
save_path = '/tmp/test_save.agent'
hook = AgentHook(agent, save_path=save_path)
assess_file_has_been_saved_on_disk_and_not_on_ram(hook, save_path)
os.remove(save_path)
def test_can_save_tql_to_memory(RPSTask, tabular_q_learning_config_dict):
agent = build_TabularQ_Agent(RPSTask, tabular_q_learning_config_dict,
'TQL')
save_path = '/tmp/test_save.agent'
hook = AgentHook(agent, save_path)
assess_file_has_been_saved_on_disk_and_not_on_ram(hook, save_path)
os.remove(save_path)
def test_can_hook_ppo_agent_using_cuda(RPSTask, ppo_config_dict):
ppo_config_dict['use_cuda'] = True
agent = build_PPO_Agent(RPSTask, ppo_config_dict, 'PPO')
assert all(
map(lambda param: param.is_cuda, agent.algorithm.model.parameters()))
hook = AgentHook(agent)
compare_against_expected_agenthook(agent, hook, AgentType.PPO,
[hook.agent.algorithm.model])
def test_can_hook_dqn_agent_using_cuda(RPSTask, dqn_config_dict):
dqn_config_dict['use_cuda'] = True
agent = build_DQN_Agent(RPSTask, dqn_config_dict, 'DQN')
assert all(
map(lambda param: param.is_cuda, agent.algorithm.model.parameters()))
assert all(
map(lambda param: param.is_cuda,
agent.algorithm.target_model.parameters()))
hook = AgentHook(agent)
compare_against_expected_agenthook(
agent, hook, AgentType.DQN,
[hook.agent.algorithm.model, hook.agent.algorithm.target_model])
def test_can_hook_tql_agent(RPSTask, tabular_q_learning_config_dict):
agent = build_TabularQ_Agent(RPSTask, tabular_q_learning_config_dict,
'TQL')
hook = AgentHook(agent)
compare_against_expected_agenthook(agent, hook, AgentType.TQL, [])