Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Continual learning is the problem of learning new tasks or knowledge while protecting old knowledge and ideally generalizing from old experience to learn new tasks faster.
Neural networks trained by stochastic gradient descent often degrade on old tasks when trained successively on new tasks with different data distributions.
This phenomenon, referred to as catastrophic forgetting, is considered a major hurdle to learning with non-stationary data or sequences of new tasks, and prevents networks from continually accumulating knowledge and skills.
We examine this issue in the context of reinforcement learning, in a setting where an agent is exposed to tasks in a sequence.
Unlike most other work, we do not provide an explicit indication to the model of task boundaries, which is the most general circumstance for a learning agent exposed to continuous experience.
While various methods to counteract catastrophic forgetting have recently been proposed, we explore a straightforward, general, and seemingly overlooked solution - that of using experience replay buffers for all past events - with a mixture of on- and off-policy learning, leveraging behavioral cloning.
We show that this strategy can still learn new tasks quickly yet can substantially reduce catastrophic forgetting in both Atari and DMLab domains, even matching the performance of methods that require task identities.
When buffer storage is constrained, we confirm that a simple mechanism for randomly discarding data allows a limited size buffer to perform almost as well as an unbounded one.
Modern day reinforcement learning (RL) has benefited substantially from a massive influx of computational resources.
In some instances, the number of data points to feed into RL algorithms has kept in step with computational feasibility.
For example, in simulation environments or in self-play RL, it is possible to generate fresh data on the fly.
In such settings, the continual learning problem (Ring, 1997) is often ignored because new experiences can be collected on demand, and the start states of the simulation can be controlled.
When training on multiple tasks, it is possible to train on all environments simultaneously within the same data batch.As RL is increasingly applied to problems in industry or other real-world settings, however, it is necessary to consider cases, such as robotics, where gathering new experience is expensive or difficult.
In such examples, simultaneous training may be infeasible.
Instead, an agent must be able to learn from only one task at a time.
The time spent on different tasks and the sequence in which those tasks occur are not under the control of the agent.
The boundaries between tasks, in fact, will often be unknown -or tasks will deform continuously and not have definite boundaries at all.
Such a paradigm for training eliminates the possibility of simultaneously acting upon and learning from several tasks, and leads to the danger of catastrophic forgetting, wherein an agent forgets what it has learned previously when it encounters a new situation.Here, we consider the setting of reinforcement learning where compute and memory resources are large, but the environment is not stationary: this may arise because an RL agent is encountering a task curriculum or sequence of unrelated tasks, engaged in a budgeted physical interaction within a robot, or learning from unstructured interaction with humans.
In this setting, the problem of continual learning rears its head: the distribution over experiences is not controlled to facilitate the agent's maintenance of previously acquired ability.An ideal continual learning system should meet three requirements.
First, it should retain previously learned capacities.
When a previously encountered task or situation is encountered, performance Figure 1 : Separate, simultaneous, and sequential training: the x-axis denotes environment steps summed across all tasks and the y-axis episode score.
In "Sequential", thick line segments are used to denote the task currently being trained, while thin segments are plotted by evaluating performance without learning.
In simultaneous training, performance on explore object locations small is higher than in separate training, an example of modest constructive interference.
In sequential training, tasks that are not currently being learned exhibit very dramatic catastrophic forgetting.
(See Appendix C for a different plot of these data.)should immediately be good -ideally as good as it was historically.
Second, maintenance of old skills or knowledge should not inhibit further rapid acquisition of a new skill or knowledge.
These two simultaneous constraints -maintaining the old while still adapting to the new -represent the challenge known as the stability-plasticity dilemma BID4 .
Third, where possible, a continual learning system should learn new skills that are related to old ones faster than it would have de novo, a property known as constructive interference or positive transfer.We here demonstrate the surprising power of a simple approach: Continual Learning with Experience And Replay (CLEAR).
We show that training a network on a mixture of novel experience on-policy and replay experience off-policy allows for both maintenance of performance on earlier tasks and fast adaptation to new tasks.
A significant further boost in performance and reduction in catastrophic forgetting is obtained by enforcing behavioral cloning between the current policy and its past self.
While memory is rarely severely limited in modern RL, we show that small replay buffers filled with uniform samples from past experiences can be almost as effective as buffers of unbounded size.
When comparing CLEAR against state-of-the-art approaches for reducing catastrophic forgetting, we obtain better or comparable results, despite the relative simplicity of our approach; yet, crucially, CLEAR requires no information about the identity of tasks or boundaries between them.
Some version of replay is believed to be present in biological brains.
We do not believe that our implementation is reflective of neurobiology, though there are potential connections; hippocampal replay has been proposed as a systems-level mechanism to reduce catastrophic forgetting and improve generalization as in the theory of complementary learning systems BID12 .
This contrasts to some degree with synapse-level consolidation, which is also believed to be present in biology BID1 , but is more like continual learning methods that protect parameters.Indeed, algorithms for continual learning may live on a Pareto frontier: different methods may have different regimes of applicability.
In cases for which storing a large memory buffer is truly prohibitive, methods that protect inferred parameters, such as Progress & Compress, may be more suitable than replay methods.
When task identities are available or boundaries between tasks are very clear, leveraging this information may reduce memory or computational demands or be useful to alert the agent to engage in rapid learning.
Further, there exist training scenarios that are adversarial .
We find that CLEAR demonstrates comparable or greater performance than these methods, despite being significantly simpler and not requiring any knowledge of boundaries between tasks.(See
Appendix C for a different plot of these data.) either to our method or to any method that prevents forgetting. For
example, if the action space of a task were changed during training, fitting to the old policy's action distribution, whether through behavioral cloning, off-policy learning, weight protection, or any of a number of other strategies for preventing catastrophic forgetting, could have a deleterious effect on future performance. For
such cases, we may need to develop algorithms that selectively protect skills as well as forget them.We have explored CLEAR in a range of continual learning scenarios; we hope that some of the experimental protocols, such as probing with a novel task at varied positions in a training sequence, may inspire other research. Moving
forward, we anticipate many algorithmic innovations that build on the ideas set forward here. For example
, weight-consolidation techniques such as Progress & Compress are quite orthogonal to our approach and could be married with it for further performance gains. Moreover,
while the V-Trace algorithm we use is effective at off-policy correction for small shifts between the present and past policy distributions, it is possible that off-policy approaches leveraging Q-functions, such as Retrace , may prove more powerful still.We have described a simple but powerful approach for preventing catastrophic forgetting in continual learning settings. CLEAR uses
on-policy learning on fresh experiences to adapt rapidly to new tasks, while using off-policy learning with behavioral cloning on replay experience to maintain and modestly enhance performance on past tasks. Behavioral
cloning on replay data further enhances the agent's stability. Our method
is simple, scalable, and practical; it takes advantage of the general abundance of memory and storage in modern computers and computing facilities. We believe
that the broad applicability and simplicity of the approach make CLEAR a candidate "first line of defense" against catastrophic forgetting in many RL contexts. Friedemann
Zenke, Ben Poole, and Surya Ganguli. Continual
learning through synaptic intelligence.In ICML, 2017.
We show that, in continual learning settings, catastrophic forgetting can be avoided by applying off-policy RL to a mixture of new and replay experience, with a behavioral cloning loss.