Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control
This paper introduces a new method for reinforcement learning that helps computers learn more efficiently by using past experiences. It shows that this method can learn faster and achieve better results in games compared to traditional methods.
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- 1 Yet another solution is to use an alternative objective, yielding a policy that balances between maximising both the expected return and its entropy over states .
- 2 We do not train on a maximum entropy objective, thereby purely using the maximum entropy mellowmax policy for exploration.
- 3 The goal of RL is to find the optimal policy, \u03c0 * , that maximizes the expected cumulative discounted return when followed from any state s \u2208 S.
- 4 We were unable to improve the uncertainty estimates by fitting a Gaussian process to the nearest neighbours and optimising over \u03b4 in the inverse distance weighted kernel.
Introduction
Despite the successes of deep reinforcement learning (DRL) agents , these models have a sampleefficiency limitation: DRL agents typically require hundreds of times more experience than a human to reach similar levels, suggesting a large gap between current DRL algorithms and the operation of the human brain . Recently, new neuro-inspired episodic control (EC) algorithms have demonstrated rapid learning, as compared to state-of-the-art DRL methods .
These algorithms were inspired by human long-term memory, which can be divided into semantic and episodic memory: the former is responsible for storing general knowledge and facts about the world, whilst the latter is related to recollecting our personal experiences .
EC, introduced by Lengyel & Dayan , is inspired by this biological episodic memory, and models one of the several different control systems used for behavioural decisions as suggested by neuroscience research .
Despite the successes of deep reinforcement learning (DRL) agents , these models have a sampleefficiency limitation: DRL agents typically require hundreds of times more experience than a human to reach similar levels, suggesting a large gap between current DRL algorithms.
Research Question
Yet another solution is to use an alternative objective, yielding a policy that balances between maximising both the expected return and its entropy over states . We do not train on a maximum entropy objective, thereby purely using the maximum entropy mellowmax policy for exploration.
Methodology
Q-learning , a widely used temporal difference (TD) method, can learn value functions by bootstrapping. As a baseline across all environments we used a strong DRL method-the dueling double DQN (D3QN) (further detailed in Section 7).
Study Design
This results in the maximum entropy mellowmax policy: \u03c0 mm (a|s) = e \u03b2Q(s,a) a\u2208A e \u03b2Q(s,a) , which is of the same functional form as the Boltzmann policy, but where the optimal \u03b2 can be solved for using a root-finding algorithm, such as Brent’s method .
For all experiments we report the mean and standard deviation of each method, calculated over three random seeds.
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Results & Findings
Without resorting to maximum entropy RL, hence decoupling exploration benefits from the objective, we show that this softmax-based exploration strategy can still improve both the sample-efficiency and final returns of EC methods, while retaining their simple, low-bias Monte Carlo returns. The goal of RL is to find the optimal policy, \u03c0 * , that maximizes the expected cumulative discounted return when followed from any state s \u2208 S.
- Without resorting to maximum entropy RL, hence decoupling exploration benefits from the objective, we show that this softmax-based exploration strategy can still improve both the sample-efficiency.
- The goal of RL is to find the optimal policy, \u03c0 * , that maximizes the expected cumulative discounted return when followed from any state s.
- Implementations of these EC methods include the non-parametric model-free EC (MFEC) , and the semi-parametric neural EC (NEC) .
- Further details on MFEC and NEC can be found in Figure 1 and Section 7.
- As we show in our experiments, this approach outperforms both the original EC methods with -greedy exploration, as well as the alternative exploration methods outlined here.
We were unable to improve the uncertainty estimates by fitting a Gaussian process to the nearest neighbours and optimising over \u03b4 in the inverse distance weighted kernel.
One limitation of mellowmax-based policies is its sensitivity to the value of \u03c9 across different domains , and the subsequent searches used to find optimal values, which are prohibitive in domains such as Atari.
Background
This section outlines the formal framework of reinforcement learning using Markov decision processes (MDPs) and describes the architecture of episodic control methods, including non-parametric and semi-parametric models. It also introduces the dueling double DQN (D3QN) as a baseline for comparison.
Exploration Strategies
The exploration strategies section details various methods for balancing exploration and exploitation in reinforcement learning, including -greedy, upper confidence bound (UCB), Thompson sampling (TS), and softmax-based policies. It discusses the advantages and challenges of each method.
Experiments
This section describes the experimental setup, including the environments used for testing the EC methods and the hyperparameters chosen for consistency across different models. It outlines the training and evaluation processes for both classic control problems and Atari games.
Frequently Asked Questions
Yet another solution is to use an alternative objective, yielding a policy that balances between maximising both the expected return and its entropy over states . We do not train on a maximum entropy objective, thereby purely using the maximum entropy mellowmax.
Atari: Figure 3 and Table 1 show the performance of our method compared to the D3QN baseline and to the -greedy policy with EC for five different Atari games. Despite this, we have retained the results for UCB in any experiments conducted.
The goal of RL is to find the optimal policy, \u03c0 * , that maximizes the expected cumulative discounted return when followed from any state s \u2208 S. We were unable to improve the uncertainty estimates by fitting a Gaussian process to.
Overall, MEMEC outperformed the other methods in these games most of the time, not only in terms of the maximum achieved reward, but also in terms of the learning speed (see Section 10 for additional learning curves).
We were unable to improve the uncertainty estimates by fitting a Gaussian process to the nearest neighbours and optimising over \u03b4 in the inverse distance weighted kernel. One limitation of mellowmax-based policies is its sensitivity to the value of \u03c9 across different.
This paper introduces a new method for reinforcement learning that helps computers learn more efficiently by using past experiences. It shows that this method can learn faster and achieve better results in games compared to traditional methods.