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Title: On-Policy Trust Region Policy Optimisation with Replay Buffers

Paper content:
Building upon the recent success of deep reinforcement learning methods, we investigate the possibility of on-policy reinforcement learning improvement by reusing the data from several consecutive policies.
On-policy methods bring many benefits, such as ability to evaluate each resulting policy.
However, they usually discard all the information about the policies which existed before.
In this work, we propose adaptation of the replay buffer concept, borrowed from the off-policy learning setting, to the on-policy algorithms.
To achieve this, the proposed algorithm generalises the Q-, value and advantage functions for data from multiple policies.
The method uses trust region optimisation, while avoiding some of the common problems of the algorithms such as TRPO or ACKTR: it uses hyperparameters to replace the trust region selection heuristics, as well as  the trainable covariance matrix instead of the fixed one.
In many cases, the method not only improves the results comparing to the state-of-the-art trust region on-policy learning algorithms such as ACKTR and TRPO, but also with respect to their off-policy counterpart DDPG.
The past few years have been marked by active development of reinforcement learning methods.
Although the mathematical foundations of reinforcement learning have been known long before BID23 , starting from 2013, the novel deep learning techniques allowed to solve vision based discrete control tasks such as Atari 2600 games BID15 as well as continuous control problems BID12 .
Many of the leading state-of-the-art reinforcement learning methods share the actor-critic architecture BID5 .
Actorcritic methods separate the actor, providing a policy, and the critic, providing an approximation for the expected discounted cumulative reward or some derived quantities such as advantage functions BID2 .
However, despite improvements, state-of-the-art reinforcement learning still suffers from poor sample efficiency and extensive parameterisation.
For most real-world applications, in contrast to simulations, there is a need to learn in real time and over a limited training period, while minimising any risk that would cause damage to the actor or the environment.Reinforcement learning algorithms can be divided into two groups: on-policy and off-policy learning.
On-policy approaches (e. g., SARSA BID18 , ACKTR BID28 ) evaluate the target policy by assuming that future actions will be chosen according to it, hence the exploration strategy must be incorporated as a part of the policy.
Off-policy methods (e. g., Qlearning BID27 , DDPG BID12 ) separate the exploration strategy, which modifies the policy to explore different states, from the target policy.The off-policy methods commonly use the concept of replay buffers to memorise the outcomes of the previous policies and therefore exploit the information accumulated through the previous iterations BID13 .
BID15 combined this experience replay mechanism with Deep Q-Networks (DQN), demonstrating end-to-end learning on Atari 2600 games.
One limitation of DQN is that it can only operate on discrete action spaces.
BID12 proposed an extension of DQN to handle continuous action spaces based on the Deep Deterministic Policy Gradient (DDPG).
There, exponential smoothing of the target actor and critic weights has been introduced to ensure stability of the rewards and critic predictions over the subsequent iterations.
In order to improve the variance of policy gradients, BID20 proposed a Generalised Advantage Function.
combined this advantage function learning with a parallelisation of exploration using differently trained actors in their Asynchronous Advantage Actor Critic model (A3C); however, BID26 demonstrated that such parallelisation may also have negative impact on sample efficiency.
Although some work has been performed on improvement of exploratory strategies for reinforcement learning BID8 , but it still does not solve the fundamental restriction of inability to evaluate the actual policy, neither it removes the necessity to provide a separate exploratory strategy as a separate part of the method.In contrast to those, state-of-the-art on-policy methods have many attractive properties: they are able to evaluate exactly the resulting policy with no need to provide a separate exploration strategy.
However, they suffer from poor sample efficiency, to a larger extent than off-policy reinforcement learning.
TRPO method BID19 has introduced trust region policy optimisation to explicitly control the speed of policy evolution of Gaussian policies over time, expressed in a form of Kullback-Leibler divergence, during the training process.
Nevertheless, the original TRPO method suffered from poor sample efficiency in comparison to off-policy methods such as DDPG.
One way to solve this issue is by replacing the first order gradient descent methods, standard for deep learning, with second order natural gradient (Amari, 1998).
BID28 used a Kroneckerfactored Approximate Curvature (K-FAC) optimiser BID14 in their ACKTR method.
PPO method proposes a number of modifications to the TRPO scheme, including changing the objective function formulation and clipping the gradients.
BID26 proposed another approach in their ACER algorithm: in this method, the target network is still maintained in the off-policy way, similar to DDPG BID12 , while the trust region constraint is built upon the difference between the current and the target network.Related to our approach, recently a group of methods has appeared in an attempt to get the benefits of both groups of methods.
BID7 propose interpolated policy gradient, which uses the weighted sum of both stochastic BID24 and deterministic policy gradient BID22 .
BID17 propose an off-policy trust region method, Trust-PCL, which exploits off-policy data within the trust regions optimisation framework, while maintaining stability of optimisation by using relative entropy regularisation.While it is a common practice to use replay buffers for the off-policy reinforcement learning, their existing concept is not used in combination with the existing on-policy scenarios, which results in discarding all policies but the last.
Furthermore, many on-policy methods, such as TRPO BID19 , rely on stochastic policy gradient BID24 , which is restricted by stationarity assumptions, in a contrast to those based on deterministic policy gradient BID22 , like DDPG BID12 .
In this article, we describe a novel reinforcement learning algorithm, allowing the joint use of replay buffers with trust region optimisation and leading to sample efficiency improvement.
The contributions of the paper are given as follows:1.
a reinforcement learning method, enabling replay buffer concept along with on-policy data;
2. theoretical insights into the replay buffer usage within the on-policy setting are discussed;
3. we show that, unlike the state-of-the-art methods as ACKTR BID28 , PPO (Schulman et al., 2017) and TRPO BID19 , a single non-adaptive set of hyperparameters such as the trust region radius is sufficient for achieving better performance on a number of reinforcement learning tasks.As we are committed to make sure the experiments in our paper are repeatable and to further ensure their acceptance by the community, we will release our source code shortly after the publication.
The paper combines replay buffers and on-policy data for reinforcement learning.
Experimental results on various tasks from the MuJoCo suite BID25 show significant improvements compared to the state of the art.
Moreover, we proposed a replacement of the heuristically calculated trust region parameters, to a single fixed hyperparameter, which also reduces the computational expences, and a trainable diagonal covariance matrix.The proposed approach opens the door to using a combination of replay buffers and trust regions for reinforcement learning problems.
While it is formulated for continuous tasks, it is possible to reuse the same ideas for discrete reinforcement learning tasks, such as ATARI games.

assistant: text: We investigate the theoretical and practical evidence of on-policy reinforcement learning improvement by reusing the data from several consecutive policies.