def test_tracks_changes_from_multiple_actions():
"""Tests that it tracks the changes as a result of actions correctly"""
agent = DQN_HER(config)
agent.reset_game()
for ix in range(4):
previous_obs = agent.observation
previous_desired_goal = agent.desired_goal
previous_achieved_goal = agent.achieved_goal
agent.action = ix
agent.conduct_action_in_changeable_goal_envs(agent.action)
assert agent.next_state.shape[0] == 8
assert isinstance(agent.next_state_dict, dict)
assert not all(agent.observation == previous_obs)
assert not all(agent.achieved_goal == previous_achieved_goal)
assert all(agent.desired_goal == previous_desired_goal)
agent.track_changeable_goal_episodes_data()
agent.save_experience()
if agent.done: agent.save_alternative_experience()
agent.state_dict = agent.next_state_dict # this is to set the state for the next iteration
agent.state = agent.next_state
states, actions, rewards, next_states, dones = agent.HER_memory.sample(4)
assert all(states[1] == torch.Tensor([1.0, 1., 1., 1., 0., 0., 0., 0.]))
assert all(actions == torch.Tensor([[1.], [0.], [3.], [2.]]))
assert all(rewards == torch.Tensor([[-1.], [-1.], [4.], [-1.]]))
assert all(dones == torch.Tensor([[0.], [0.], [1.], [0.]]))
def test_initiation():
"""Tests whether DQN_HER initiates correctly"""
config.hyperparameters["batch_size"] = 64
agent = DQN_HER(config)
agent.reset_game()
assert agent.ordinary_buffer_batch_size == int(0.2 * 64)
assert agent.HER_buffer_batch_size == 64 - int(0.2 * 64)
assert agent.q_network_local.input_dim == 8
assert agent.q_network_local.output_layers[0].out_features == 4
assert isinstance(agent.state_dict, dict)
assert agent.observation.shape[0] == 4
assert agent.desired_goal.shape[0] == 4
assert agent.achieved_goal.shape[0] == 4
assert agent.state.shape[0] == 8
assert not agent.done
assert agent.next_state is None
assert agent.reward is None
config.hyperparameters["batch_size"] = 3
"beta_prioritised_replay": 0.4,
"incremental_td_error": 1e-8,
"update_every_n_steps": 3,
"linear_hidden_units": [20, 20, 20],
"final_layer_activation": "None",
"batch_norm": False,
"gradient_clipping_norm": 5,
"HER_sample_proportion": 0.8,
"clip_rewards": False
}
}
trainer = Trainer(config, [DQN_HER])
config.hyperparameters = config.hyperparameters["DQN_Agents"]
agent = DQN_HER(config)
agent.reset_game()
def test_initiation():
"""Tests whether DQN_HER initiates correctly"""
config.hyperparameters["batch_size"] = 64
agent = DQN_HER(config)
agent.reset_game()
assert agent.ordinary_buffer_batch_size == int(0.2 * 64)
assert agent.HER_buffer_batch_size == 64 - int(0.2 * 64)
assert agent.q_network_local.input_dim == 8
assert agent.q_network_local.output_layers[0].out_features == 4
assert isinstance(agent.state_dict, dict)