Can tf.agent policy return probability vector for all actions?

Question

I am trying to train a Reinforcement Learning agent using TF-Agent TF-Agent DQN Tutorial. In my application, I have 1 action containing 9 possible discrete values (labeled from 0 to 8). Below is the output from env.action_spec()

BoundedTensorSpec(shape=(), dtype=tf.int64, name='action', minimum=array(0, dtype=int64), maximum=array(8, dtype=int64))

I would like to get the probability vector contains all actions calculated by the trained policy, and do further processing in other application environments. However, the policy only returns log_probability with a single value rather than a vector for all actions. Is there anyway to get the probability vector?

from tf_agents.networks import q_network
from tf_agents.agents.dqn import dqn_agent

q_net = q_network.QNetwork(
            env.observation_spec(),
            env.action_spec(),
            fc_layer_params=(32,)
        )

optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=0.001)

my_agent = dqn_agent.DqnAgent(
    env.time_step_spec(),
    env.action_spec(),
    q_network=q_net,
    epsilon_greedy=epsilon,
    optimizer=optimizer,
    emit_log_probability=True,
    td_errors_loss_fn=common.element_wise_squared_loss,
    train_step_counter=global_step)

my_agent.initialize()

...  # training

tf_policy_saver = policy_saver.PolicySaver(my_agent.policy)
tf_policy_saver.save('./policy_dir/')

# making decision using the trained policy
action_step = my_agent.policy.action(time_step)

In dqn_agent.DqnAgent() DQNAgent, I set emit_log_probability=True, which is supposed to define Whether policies emit log probabilities or not.

However, when I run action_step = my_agent.policy.action(time_step), it returns

PolicyStep(action=<tf.Tensor: shape=(1,), dtype=int64, numpy=array([1], dtype=int64)>, state=(), info=PolicyInfo(log_probability=<tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.], dtype=float32)>))

I also tried to run action_distribution = saved_policy.distribution(time_step), It returns

PolicyStep(action=<tfp.distributions.DeterministicWithLogProbCT 'Deterministic' batch_shape=[1] event_shape=[] dtype=int64>, state=(), info=PolicyInfo(log_probability=<tf.Tensor: shape=(), dtype=float32, numpy=0.0>))

If there is no such API available in TF.Agent, is there a way to get such probability vector? Thanks.

Follow-up Question:

If I understand correctly, deep Q-network is supposed to get inputs of the state and output the Q-value for each action from the state. I could pass this Q-value vector into a softmax function and calculate the corresponding probability vector. Actually I have done such calculation with my own customized DQN script (without TF-Agent). Then the question becomes: how to return the Q-value vector from TF-Agent?

Answer 1

The only way to do this in the TF-Agents framework is to invoke the Policy.distribution() method instead of the action method. This would return the original distribution that was computed out of the Q-values of the network. The emit_log_probability=True only affects the info attribute of the PolicyStep namedtuple that Policy.action() returns. Note that this distribution is possibly affected by the action constraints that you pass (if you do); whereby illegal actions will be marked as having 0 probability (even though there original Q-value might have been high).

If furthermore you would like to see the actual Q-values instead of the distribution that they generate, then I'm afraid there is no way of doing this without acting directly upon the Q-network that comes with your agent (and that is also attached to the Policy object that the agent produces). If you want to see how to call that Q-network properly I recommend looking at how the QPolicy._distribution() method does it here.

Note that none of this can be done using the pre-implemented Drivers. You would have to either explicitly construct your own collection loop or implement your own Driver object (which is basically equivalent).