This is brief technical note on how to get convergence in the cooperative inverse reinforcement learning framework. We extend cooperative inverse RL to partially observable domains and use a recent result on the grain of truth problem to establish (arguably very strong) conditions to get convergence to -Nash equilibria.

Credit: this came out of the CSRBAI Workshop on Preference Specification. Several people contributed ideas, in particular Vadim Kosoy.


We use the setup and notation from the grain of truth paper (Leike, Taylor, and Fallenstein, 2016). We only review the most important notation here in an attempt to make this post notationally fairly self-contained. The set denotes a countable class of stochastic environments, the class of all environments that are reflective-oracle-computable (computable on a probabilistic Turing machine with access to a reflective oracle).

Let be any two-player environment. Let denote the human player and let denote the robot player. Each player interacts with the environment in cycles: at time step the player chooses an action and receives a percept the cycle then repeats for . A history is an element of . We use to denote one interaction cycle, and to denote a history of length . We assume the sets and are finite.

The human follows a policy and the robot follows a policy . The human acts in the subjective environment (environment combined with the robot) and the robot acts in the subjective environment (environment combined with the human). Each player does not observe the action and percept of the other player directly.

Moreover, only the human sees the reward signal, not the robot. Yet they both try to maximize this signal; in this sense they are playing a cooperative game. We assume that the reward is uniquely determined by the robot's history (the robot has all the necessary information available to determine the reward). The robot has a prior over reward functions that includes the true reward function.

One question is how to get a reward signal to the robot. We assume that the robot maximizes the belief reward signal: For any prior on reward functions, the robot can calculate at every time step the -expected reward obtained. We let the robot maximize the belief reward signal. This is of course not actually desirable, because it provides no extra incentive to the robot to take actions that lead to learning the human's actual reward function. We use to denote the robot's subjective environment augmented with the -expected reward signal.

We fix a discount function with and . The goal is to maximize discounted rewards , where denotes the human's reward at time step . The discount normalization factor is defined as . We define the value function as follows. For the robot, we use to denote the policy 's subjective value (from the robot's point of view) and to denote the policy 's actual value (in terms of the rewards the human receives).


Our result relies on two assumptions. We discuss them in turn.

Assumption 1 (Human is AO). Player is asymptotically optimal in mean in the environment class : for all .

On the one hand, Assumption 1 feels too strong: One of the core ideas of value learning is that the AI is more powerful than the human, and whether value learning succeeds should not hinge on whether the human learns to behave optimally in the limit. On the other hand, maybe assuming a superintelligence-assisted human becomes asymptotically optimal is not so unrealistic: after all, it is just saying that the human would use the robot to get as much reward as possible.

Assumption 2 (Teachability). For all and all policies , if infinitely often, then infinitely often.

The teachability assumption states that if the robot's belief value differs from its actual value by more than (on any policy), then there is a sequence of actions that the human can take to teach the robot, and that it is suboptimal for the human not to do so. This means that the effective horizon has to be long enough for the human to provide information to the robot, for the robot to change its behavior, and for both of them to adopt better policies.

The form our techability assumption takes is somewhat cheating, because it packages a bunch of steps into one assumption. Future work should try to unpack it and make several smaller assumptions that are easier to understand.

Theorem. If Assumption 1 and Assumption 2 are satisfied and the human is reflective-oracle-computable, then there is a policy for the robot such that for any both human and robot converge to an -Nash equilibria in probability.


The proof is a relatively straightforward application of existing results. The robot maximizes the belief reward signal; as its policy we choose Thompson sampling because we know that Thompson sampling is asymptotically optimal in mean in any countable class of stochastic environments, in particular (Leike et al., 2016, Thm. 4). Moreover, Thompson sampling is reflective-oracle-computable. Therefore we get that (both and have a grain of truth). From Assumption 1 we get that the human is also asymptotically optimal in mean. Now we can apply Theorem 28 from Leike, Taylor, and Fallenstein (2016) to get that for all the probability that both human and robot play an -best response converges to . However, this is not necessarily a -Nash equilibrium yet because the robot is only best responding in its belief environment, which might be inaccurate. In other words, we get , but we want . This is where Assumption 2 comes in. Together with Assumption 1 it provides that for any policy . Omitting history arguments we write The first convergence is from Assumption 2, the second from the robot's asymptotic optimality, and the third is from Assumption 2 again.

Open Questions

  • The teachability assumption seems too strong. Can we unpack it further? Moreover, currently we require it to hold off-policy.
  • Convergence to a Nash equilibrium is not very strong. How can we ensure that this Nash equilibrium is Pareto efficient?
  • How can we put incentives for the robot to actively learn the human's reward function into the model?
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