add inital coma optimizer tests

ml-agents/mlagents/trainers/torch/networks.py

         Since these are raw obs, the padded values are still NaN
         """
         only_first_obs = [_all_obs[0] for _all_obs in obs_tensors]
-        obs_for_mask = torch.stack(only_first_obs, dim=1)
-        # Get the mask from nans
-        attn_mask = torch.any(obs_for_mask.isnan(), dim=2).type(torch.FloatTensor)
+        # flatten for correct dimensions with visual obs
+        only_first_obs_flat = torch.stack(
+            [_obs.flatten(start_dim=1)[:, 0] for _obs in only_first_obs], dim=1
+        )
+        attn_mask = only_first_obs_flat.isnan().type(torch.FloatTensor)
         return attn_mask
 
     def forward(

ml-agents/mlagents/trainers/tests/torch/test_coma.py

+import pytest
+
+import numpy as np
+import attr
+
+from mlagents.trainers.coma.optimizer_torch import TorchCOMAOptimizer
+from mlagents.trainers.settings import RewardSignalSettings, RewardSignalType
+
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.tests import mock_brain as mb
+from mlagents.trainers.tests.mock_brain import copy_buffer_fields
+from mlagents.trainers.tests.test_trajectory import make_fake_trajectory
+from mlagents.trainers.settings import NetworkSettings
+from mlagents.trainers.tests.dummy_config import (  # noqa: F401
+    ppo_dummy_config,
+    curiosity_dummy_config,
+    gail_dummy_config,
+)
+
+from mlagents_envs.base_env import ActionSpec
+from mlagents.trainers.buffer import BufferKey, RewardSignalUtil
+
+
+@pytest.fixture
+def dummy_config():
+    # coma has the same hyperparameters as ppo for now
+    return ppo_dummy_config()
+
+
+VECTOR_ACTION_SPACE = 2
+VECTOR_OBS_SPACE = 8
+DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
+BUFFER_INIT_SAMPLES = 64
+NUM_AGENTS = 4
+
+CONTINUOUS_ACTION_SPEC = ActionSpec.create_continuous(VECTOR_ACTION_SPACE)
+DISCRETE_ACTION_SPEC = ActionSpec.create_discrete(tuple(DISCRETE_ACTION_SPACE))
+
+
+def create_test_coma_optimizer(dummy_config, use_rnn, use_discrete, use_visual):
+    mock_specs = mb.setup_test_behavior_specs(
+        use_discrete,
+        use_visual,
+        vector_action_space=DISCRETE_ACTION_SPACE
+        if use_discrete
+        else VECTOR_ACTION_SPACE,
+        vector_obs_space=VECTOR_OBS_SPACE,
+    )
+
+    trainer_settings = attr.evolve(dummy_config)
+    trainer_settings.reward_signals = {
+        RewardSignalType.GROUP_EXTRINSIC: RewardSignalSettings(strength=1.0, gamma=0.99)
+    }
+
+    trainer_settings.network_settings.memory = (
+        NetworkSettings.MemorySettings(sequence_length=16, memory_size=10)
+        if use_rnn
+        else None
+    )
+    policy = TorchPolicy(0, mock_specs, trainer_settings, "test", False)
+    optimizer = TorchCOMAOptimizer(policy, trainer_settings)
+    return optimizer
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
+@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
+def test_coma_optimizer_update(dummy_config, rnn, visual, discrete):
+    # Test evaluate
+    optimizer = create_test_coma_optimizer(
+        dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
+    )
+    # Test update
+    update_buffer = mb.simulate_rollout(
+        BUFFER_INIT_SAMPLES,
+        optimizer.policy.behavior_spec,
+        memory_size=optimizer.policy.m_size,
+        num_other_agents_in_group=NUM_AGENTS,
+    )
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        update_buffer,
+        BufferKey.ENVIRONMENT_REWARDS,
+        [
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.returns_key("group"),
+            RewardSignalUtil.value_estimates_key("group"),
+            RewardSignalUtil.baseline_estimates_key("group"),
+        ],
+    )
+    # Copy memories to critic memories
+    copy_buffer_fields(update_buffer, BufferKey.MEMORY, [BufferKey.CRITIC_MEMORY])
+
+    return_stats = optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+    # Make sure we have the right stats
+    required_stats = [
+        "Losses/Policy Loss",
+        "Losses/Value Loss",
+        "Policy/Learning Rate",
+        "Policy/Epsilon",
+        "Policy/Beta",
+    ]
+    for stat in required_stats:
+        assert stat in return_stats.keys()
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
+@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
+def test_coma_get_value_estimates(dummy_config, rnn, visual, discrete):
+    optimizer = create_test_coma_optimizer(
+        dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
+    )
+    time_horizon = 15
+    trajectory = make_fake_trajectory(
+        length=time_horizon,
+        observation_specs=optimizer.policy.behavior_spec.observation_specs,
+        action_spec=DISCRETE_ACTION_SPEC if discrete else CONTINUOUS_ACTION_SPEC,
+        max_step_complete=True,
+        num_other_agents_in_group=NUM_AGENTS,
+    )
+    run_out, final_value_out, all_memories = optimizer.get_trajectory_and_baseline_value_estimates(
+        trajectory.to_agentbuffer(), trajectory.next_obs, done=False
+    )
+    for key, val in run_out.items():
+        assert type(key) is str
+        assert len(val) == 15
+    if all_memories is not None:
+        assert len(all_memories) == 15
+
+    run_out, final_value_out, _ = optimizer.get_trajectory_and_baseline_value_estimates(
+        trajectory.to_agentbuffer(), trajectory.next_obs, done=True
+    )
+    for key, val in final_value_out.items():
+        assert type(key) is str
+        assert val == 0.0
+
+    # Check if we ignore terminal states properly
+    optimizer.reward_signals["group"].use_terminal_states = False
+    run_out, final_value_out, _ = optimizer.get_trajectory_and_baseline_value_estimates(
+        trajectory.to_agentbuffer(), trajectory.next_obs, done=False
+    )
+    for key, val in final_value_out.items():
+        assert type(key) is str
+        assert val != 0.0
+
+
+@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
+@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
+@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
+# We need to test this separately from test_reward_signals.py to ensure no interactions
+def test_ppo_optimizer_update_curiosity(
+    dummy_config, curiosity_dummy_config, rnn, visual, discrete  # noqa: F811
+):
+    # Test evaluate
+    dummy_config.reward_signals = curiosity_dummy_config
+    optimizer = create_test_coma_optimizer(
+        dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
+    )
+    # Test update
+    update_buffer = mb.simulate_rollout(
+        BUFFER_INIT_SAMPLES,
+        optimizer.policy.behavior_spec,
+        memory_size=optimizer.policy.m_size,
+    )
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        update_buffer,
+        src_key=BufferKey.ENVIRONMENT_REWARDS,
+        dst_keys=[
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.returns_key("extrinsic"),
+            RewardSignalUtil.value_estimates_key("extrinsic"),
+            RewardSignalUtil.returns_key("curiosity"),
+            RewardSignalUtil.value_estimates_key("curiosity"),
+        ],
+    )
+    # Copy memories to critic memories
+    copy_buffer_fields(update_buffer, BufferKey.MEMORY, [BufferKey.CRITIC_MEMORY])
+
+    optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+
+
+# We need to test this separately from test_reward_signals.py to ensure no interactions
+def test_ppo_optimizer_update_gail(gail_dummy_config, dummy_config):  # noqa: F811
+    # Test evaluate
+    dummy_config.reward_signals = gail_dummy_config
+    config = ppo_dummy_config()
+    optimizer = create_test_coma_optimizer(
+        config, use_rnn=False, use_discrete=False, use_visual=False
+    )
+    # Test update
+    update_buffer = mb.simulate_rollout(
+        BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec
+    )
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        update_buffer,
+        src_key=BufferKey.ENVIRONMENT_REWARDS,
+        dst_keys=[
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.returns_key("extrinsic"),
+            RewardSignalUtil.value_estimates_key("extrinsic"),
+            RewardSignalUtil.returns_key("gail"),
+            RewardSignalUtil.value_estimates_key("gail"),
+        ],
+    )
+
+    update_buffer[BufferKey.CONTINUOUS_LOG_PROBS] = np.ones_like(
+        update_buffer[BufferKey.CONTINUOUS_ACTION]
+    )
+    optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+
+    # Check if buffer size is too big
+    update_buffer = mb.simulate_rollout(3000, optimizer.policy.behavior_spec)
+    # Mock out reward signal eval
+    copy_buffer_fields(
+        update_buffer,
+        src_key=BufferKey.ENVIRONMENT_REWARDS,
+        dst_keys=[
+            BufferKey.ADVANTAGES,
+            RewardSignalUtil.returns_key("extrinsic"),
+            RewardSignalUtil.value_estimates_key("extrinsic"),
+            RewardSignalUtil.returns_key("gail"),
+            RewardSignalUtil.value_estimates_key("gail"),
+        ],
+    )
+    optimizer.update(
+        update_buffer,
+        num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
+    )
+
+
+if __name__ == "__main__":
+    pytest.main()