Paper:
Learning useful skills without a manually-designed reward function would have many applications, yet is still a challenge for reinforcement learning. In this paper, we propose Mutual Information-based State-Control (MISC), a new selfsupervised Reinforcement Learning approach for learning to control states of interest without any external reward function. We formulate the intrinsic objective as rewarding the skills that maximize the mutual information between the context states and the states of interest. For example, in robotic manipulation tasks, the context states are the robot states and the states of interest are the states of an object. We evaluate our approach for different simulated robotic manipulation tasks from OpenAI Gym and a navigation task in the Gazebo simulator. We show that our method is able to learn to manipulate the object, such as pushing and picking up, purely based on the intrinsic mutual information rewards. Furthermore, the pre-trained policy and mutual information discriminator can be used to accelerate learning to achieve high task rewards. Our results show that the mutual information between the context states and the states of interest can be an effective ingredient for overcoming challenges in robotic manipulation tasks with sparse rewards. A video showing experimental results is available at https://youtu.be/l5KaYJWWu70.

Review:
This paper proposes a self-supervised reinforcement learning approach, Mutual Information-based State-Control (MISC), which maximizes the mutual information between the context states (i.e. robot states) and the states of interest (i.e. states of an object to manipulate). For this, they first split the entire state into two mutually exclusive sets of the context states and the states of interest. Then, the neural discriminator is trained to estimate the (lower-bound of) mutual information between the two states. The (mutual-information) intrinsic reward is computed by the trained neural discriminator, which is used for policy pre-training. Experimental results show that MISC helps to improve the performance of DDPG/SAC and the learned discriminator can be transferred to different environments.
Detailed comments and questions:
- In the paper, the states are represented by only object positions (x, y, z). Is this sufficient? (e.g. velocity is unnecessary?)
- For MISC, the additional assumption is required: the agent should know that which parts of the states are its own controllable state and object's state respectively. Is this additional assumption realistic enough and has it been adopted in other previous works? Is there any way to discriminate robot states and object states automatically?
- Can MISC deal with the problems where the number of objects of interest is more than two? In this case, how can we define mutual information?
- In Eq. (4), T(x_1:N, y_1:N) is assumed to be decomposable into the sum of T(x_t, y_t) / N. Can this make the lower bound (Eq. (3)) arbitrarily loose since the class of functions becomes very limited?
- Detailed experimental setups are missing. e.g. network architecture, hyper-parameters (e.g. I_tran^max), and how they were searched.
- Similarly to the problem of sparse reward, if the robot and the object are far apart and it is difficult to reach the object with random exploration, it would also be difficult to train the mutual information discriminator. How was the discriminator trained? How many time steps were used to train MI discriminator?
- It seems that the MI discriminator learns to estimate the 'proximity' between the robot and the object. Compared to using just a very simple dense reward (e.g. negative L2 distance between the robot and the object), what would be the advantage of using MI discriminator? It would be great to show the comparison between using simple dense reward and MI discriminator for each Push, Pick&Place, and Slide task.
- For the MISC+DIAYN, what if we train the agent using MISC and DIAYN at the same time, instead of pre-training MISC first and fine-tuning DIAYN later?
- It is unclear how MISC-p is performed. Please elaborate on how MISC-p works for prioritization.
- Also, for MISC-r experiments, the weights between the intrinsic reward bonus and the extrinsic reward are not specified in the paper.
- It seems that MISC is beneficial when the robot should get closer to the object for the success of the task. Then, how about the opposite situation? What if the task requires that the robot should 'avoid' the object of interest? Does MISC still work? Is it helpful for the improvement of sample efficiency?
- In order to pre-train the discriminator network, additional (s,a,s') experiences are required, thus it seems difficult to say that it is better for exploration than VIME.
- In section 4.3, what happens if we transfer the learned discriminator to Pick&Place from Push that has a gripper fixed to be closed, rather than the opposite direction (i.e. from Pick&Place to Push)? Does the MISC-t still well work? Can the learned MI discriminator be transferred to different tasks even when the state space is different?