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Title: Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning

Paper content:
Off-policy reinforcement learning algorithms promise to be applicable in settings where only a fixed data-set (batch) of environment interactions is available and no new experience can be acquired.
This property makes these algorithms appealing for real world problems such as robot control.
In practice, however, standard off-policy algorithms fail in the batch setting for continuous control.
In this paper, we propose a simple solution to this problem.
It admits the use of data generated by arbitrary behavior policies and uses a learned prior -- the advantage-weighted behavior model (ABM) -- to bias the RL policy towards actions that have previously been executed and are likely to be successful on the new task.
Our method can be seen as an extension of recent work on batch-RL that enables stable learning from conflicting data-sources.
We find  improvements on competitive baselines in a variety of RL tasks -- including standard continuous control benchmarks and multi-task learning for simulated and real-world robots.
Batch reinforcement learning (RL) (Ernst et al., 2005; Lange et al., 2011) is the problem of learning a policy from a fixed, previously recorded, dataset without the opportunity to collect new data through interaction with the environment.
This is in contrast to the typical RL setting which alternates between policy improvement and environment interaction (to acquire data for policy evaluation).
In many real world domains collecting new data is laborious and costly, both in terms of experimentation time and hardware availability but also in terms of the human labour involved in supervising experiments.
This is especially evident in robotics applications (see e.g. Haarnoja et al. 2018b; Kalashnikov et al. 2018 for recent examples learning on robots).
In these settings where gathering new data is expensive compared to the cost of learning, batch RL promises to be a powerful solution.
There exist a wide class of off-policy algorithms for reinforcement learning designed to handle data generated by a behavior policy µ which might differ from π, the policy that we are interested in learning (see e.g. Sutton & Barto (2018) for an introduction).
One might thus expect solving batch RL to be a straightforward application of these algorithms.
Surprisingly, for batch RL in continuous control domains, however, Fujimoto et al. (2018) found that policies obtained via the naïve application of off-policy methods perform dramatically worse than the policy that was used to generate the data.
This result highlights the key challenge in batch RL: we need to exhaustively exploit the information that is in the data but avoid drawing conclusions for which there is no evidence (i.e. we need to avoid over-valuing state-action sequences not present in the training data).
As we will show in this paper, the problems with existing methods in the batch learning setting are further exacerbated when the provided data contains behavioral trajectories from different policies µ 1 , . . . , µ N which solve different tasks, or the same task in different ways (and thus potentially execute conflicting actions) that are not necessarily aligned with the target task that π should accomplish.
We empirically show that previously suggested adaptations for off-policy learning (Fujimoto et al., 2018; Kumar et al., 2019) can be led astray by behavioral patterns in the data that are consistent (i.e. policies that try to accomplish a different task or a subset of the goals for the target task) but not relevant for the task at hand.
This situation is more damaging than learning from noisy or random data where the behavior policy is sub-optimal but is not predictable, i.e. the randomness is not a correlated signal that will be picked up by the learning algorithm.
We propose to solve this problem by restricting our solutions to 'stay close to the relevant data'.
This is done by:
1) learning a prior that gives information about which candidate policies are potentially supported by the data (while ensuring that the prior focuses on relevant trajectories),
2) enforcing the policy improvement step to stay close to the learned prior policy.
We propose a policy iteration algorithm in which the prior is learned to form an advantage-weighted model of the behavior data.
This prior biases the RL policy towards previously experienced actions that also have a high chance of being successful in the current task.
Our method enables stable learning from conflicting data sources and we show improvements on competitive baselines in a variety of RL tasks -including standard continuous control benchmarks and multi-task learning for simulated and real-world robots.
We also find that utilizing an appropriate prior is sufficient to stabilize learning; demonstrating that the policy evaluation step is implicitly stabilized when a policy iteration algorithm is used -as long as care is taken to faithfully evaluate the value function within temporal difference calculations.
This results in a simpler algorithm than in previous work (Fujimoto et al., 2018; Kumar et al., 2019) .
In this work, we considered the problem of stable learning from logged experience with off-policy RL algorithms.
Our approach consists of using a learned prior that models the behavior distribution contained in the data (the advantage weighted behavior model) towards which the policy of an RL algorithm is regularized.
This allows us to avoid drawing conclusions for which there is no evidence in the data.
Our approach is robust to large amounts of sub-optimal data, and compares favourably to strong baselines on standard continuous control benchmarks.
We further demonstrate that our approach can work in challenging robot manipulation domains -learning some tasks without ever seeing a single trajectory for them.
A ALGORITHM A full algorithm listing for our procedure is given in Algorithm 1.

assistant: text: We develop a method for stable offline reinforcement learning from logged data. The key is to regularize the RL policy towards a learned "advantage weighted" model of the data.