Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning

Optimism in the face of uncertainty is a principled approach for provably efficient exploration for reinforcement learning in tabular and linear settings . However , such an approach is challenging in developing practical exploration algorithms for Deep Reinforcement Learning ( DRL ) . To address this problem , we propose an Optimistic Exploration algorithm with Backward Bootstrapped Bonus ( OEB3 ) for DRL by following these two principles . OEB3 is built on bootstrapped deep Q-learning , a non-parametric posterior sampling method for temporally-extended exploration . Based on such a temporally-extended exploration , we construct an UCB-bonus indicating the uncertainty of Q-functions . The UCB-bonus is further utilized to estimate an optimistic Q-value , which encourages the agent to explore the scarcely visited states and actions to reduce uncertainty . In the estimation of Q-function , we adopt an episodic backward update strategy to propagate the future uncertainty to the estimated Q-function consistently . Extensive evaluations show that OEB3 outperforms several state-of-the-art exploration approaches in MNIST maze and 49 Atari games . 1 INTRODUCTION . In Reinforcement learning ( RL ) ( Sutton & Barto , 2018 ) formalized by the Markov decision process ( MDP ) , an agent aims to maximize the long-term reward by interacting with the unknown environment . The agent takes actions according to the knowledge of experiences , which leads to the fundamental problem of the exploration-exploitation dilemma . An agent may choose the best decision given current information or acquire more information by exploring the poorly understood states and actions . Exploring the environment may sacrifice immediate rewards but potentially improves future performance . The exploration strategy is crucial for the RL agent to find the optimal policy . The theoretical RL offers various provably efficient exploration methods in tabular and linear MDPs with the basic value iteration algorithm : least-squares value iteration ( LSVI ) . Optimism in the face of uncertainty ( Auer & Ortner , 2007 ; Jin et al. , 2018 ) is a principled approach . In tabular cases , the optimism-based methods incorporate upper confidence bound ( UCB ) into the value functions as bonuses and attain the optimal worst-case regret ( Azar et al. , 2017 ; Jaksch et al. , 2010 ; Dann & Brunskill , 2015 ) . Randomized value function based on posterior sampling chooses actions according to the randomly sampled statistically plausible value functions and is known to achieve near-optimal worst-case and Bayesian regrets ( Osband & Van Roy , 2017 ; Russo , 2019 ) . Recently , the theoretical analyses in tabular cases are extended to linear MDP where the transition and the reward function are assumed to be linear . In linear cases , optimistic LSVI ( Jin et al. , 2020 ) attains a near-optimal worst-case regret by using a designed bonus , which is provably efficient . Randomized LSVI ( Zanette et al. , 2020 ) also attains a near-optimal worst-case regret . Although the analyses in tabular and linear cases provide attractive approaches for efficient exploration , these principles are still challenging in developing a practical exploration algorithm for Deep Reinforcement Learning ( DRL ) ( Mnih et al. , 2015 ) , which achieves human-level performance in large-scale tasks such as Atari games and robotic tasks . For example , in linear cases , the bonus in optimistic LSVI ( Jin et al. , 2020 ) and nontrivial noise in randomized LSVI ( Zanette et al. , 2020 ) are tailored to linear models ( Abbasi-Yadkori et al. , 2011 ) , and are incompatible with practically powerful function approximations such as neural networks . To address this problem , we propose the Optimistic Exploration algorithm with Backward Bootstrapped Bonus ( OEB3 ) for DRL . OEB3 is an instantiation of optimistic LSVI ( Jin et al. , 2020 ) in DRL by using a general-purpose UCB-bonus to provide an optimistic Q-value and a randomized value function to perform temporally-extended exploration . We propose an UCB-bonus that represents the disagreement of bootstrapped Q-functions ( Osband et al. , 2016 ) to measure the epistemic uncertainty of the unknown optimal value function . Q-value added by UCB-bonus becomes an optimistic Q+ function that is higher than Q for scarcely visited state-action pairs and remains close to Q for frequently visited ones . The optimistic Q+ function encourages the agent to explore the states and actions with high UCB-bonuses , signifying scarcely visited areas or meaningful events in completing a task . We propose an extension of episodic backward update ( Lee et al. , 2019 ) to propagate the future uncertainty to the estimated action-value function consistently within an episode . The backward update also enables OEB3 to perform highly sample-efficient training . Comparing to existing count-based and curiosity-driven exploration methods ( Taiga et al. , 2020 ) , OEB3 has several benefits . ( 1 ) We utilize intrinsic rewards to produce optimistic value function and also take advantage of bootstrapped Q-learning to perform temporally-consistent exploration while existing methods do not combine these two principles . ( 2 ) The UCB-bonus measures the disagreement of Q-values , which considers the long-term uncertainty in an episode rather than the single-step uncertainty used in most bonus-based methods ( Pathak et al. , 2019 ; Burda et al. , 2019b ) . Meanwhile , the UCB-bonus is computed without introducing additional modules compared to bootstrapped DQN . ( 3 ) We provide a theoretical analysis showing that OEB3 has theoretical consistency with optimistic LSVI in linear cases . ( 4 ) Extensive evaluations show that OEB3 outperforms several state-of-the-art exploration approaches in MNIST maze and 49 Atari games . 2 BACKGROUND . In this section , we introduce bootstrapped DQN ( Osband et al. , 2016 ) , which we utilize in OEB3 for temporarily-extended exploration . We further introduce optimistic LSVI ( Jin et al. , 2020 ) , which we instantiate via DRL and propose OEB3 . 2.1 BOOTSTRAPPED DQN . We consider an episodic MDP represented as ( S , A , T , P , r ) , where T ∈ Z+ is the episode length , S is the state space , A is the action space , r is the reward function , and P is the unknown dynamics . In each timestep , the agent obtains the current state st , takes an action at , interacts with the environment , receives a reward rt , and updates to the next state st+1 . The action-value function Qπ ( st , at ) : = Eπ [ ∑T−1 i=t γ i−tri ] represents the expected cumulative reward starting from state st , taking action at , and thereafter following policy π ( at|st ) until the end of the episode . γ ∈ [ 0 , 1 ) is the discount factor . The optimal value function Q∗ = maxπ Qπ , and the optimal action a∗ = arg maxa∈AQ ∗ ( s , a ) . DeepQ-Network ( DQN ) uses a deep neural network with parameters θ to approximate theQ-function . The loss function takes the form of L ( θ ) = E [ ( yt − Q ( st , at ; θ ) ) 2 ∣∣ ( st , at , rt , st+1 ) ∼ D ] , where yt = rt + γmaxa′ Q ( st+1 , a ′ ; θ− ) is the target value , and θ− is the parameter of the target network . The agent accumulates experiences ( st , at , rt , st+1 ) in a replay buffer D and samples mini-batches in training . Bootstrapped DQN ( Osband et al. , 2016 ; 2018 ) is a non-parametric posterior sampling method , which maintains K estimations of Q-values to represent the posterior distribution of the randomized value function . Bootstrapped DQN uses a multi-head network that contains a shared convolution network and K heads . Each head defines a Qk-function . Bootstrapped DQN diversifies different Qk by using different random initialization and individual target networks . The loss for training Qk is L ( θk ) = E [ ( rt + γmax a′ Qk ( st+1 , a ′ ; θk− ) −Qk ( st , at ; θk ) ) 2∣∣∣ ( st , at , rt , st+1 ) ∼ D ] . ( 1 ) The k-th head Qk ( s , a ; θk ) is trained with its own target network Qk ( s , a ; θk− ) . If k-th head is sampled at the start of an episode when interacting with the environment , the agent will follow Qk to choose actions in the whole episode , which provides temporally-consistent exploration for DRL . Algorithm 1 Optimistic LSVI in linear MDP 1 : Initialize : Λt ← λ · I and wh ← 0 2 : for episode m = 0 to M − 1 do 3 : Receive the initial state s0 4 : for step t = 0 to T − 1 do 5 : Take action at = arg maxa∈AQt ( st , a ) and observe st+1 . 6 : end for 7 : for step t = T − 1 to 0 do 8 : Λt ← ∑m τ=0 φ ( x τ t , a τ t ) φ ( x τ t , a τ t ) > + λ · I 9 : wt ← Λ−1t ∑m τ=0 φ ( x τ t , a τ t ) [ rt ( x τ t , a τ t ) + maxaQt+1 ( x τ t+1 , a ) ] 10 : Qt ( · , · ) = min { w > t φ ( · , · ) + α [ φ ( · , · ) > Λ−1t φ ( · , · ) ] 1/2 , T } 11 : end for 12 : end for 2.2 OPTIMISTIC LSVI . Optimistic LSVI ( Jin et al. , 2020 ) uses an optimistic Q-value with LSVI in linear MDP . We denote the feature map of the state-action pair as φ : S ×A → Rd . The transition kernel and reward function are assumed to be linear in φ . Optimistic LSVI shown in Algorithm 1 consists of two parts . In the first part ( line 3-6 ) , the agent executes the policy according to Qt for an episode . In the second part ( line 7-11 ) , the parameter wt of Q-function is updated in closed-form by following the regularized least-squares problem wt ← arg min w∈Rd ∑m τ=0 [ rt ( s τ t , a τ t ) + max a∈A Qt+1 ( s τ t+1 , a ) − w > φ ( sτt , aτt ) ] 2 + λ‖w‖2 , ( 2 ) where m is the number of episodes , and τ is the episodic index . The least-squares problem has the explicit solution wt = Λ−1t ∑m τ=0 φ ( x τ t , a τ t ) [ rt ( x τ t , a τ t ) + maxaQt+1 ( x τ t+1 , a ) ] ( line 9 ) , where Λt is the Gram matrix . Then the value function is estimated by Qt ( s , a ) ≈ w > t φ ( s , a ) . Optimistic LSVI uses a bonus α [ φ ( s , a ) > Λ−1t φ ( s , a ) ] 1/2 ( line 10 ) to measure the uncertainty of state-action pairs . We can intuitively consider u : = ( φ > Λ−1t φ ) −1 as a pseudo count of state-action pair by projecting the total features that have observed ( Λt ) to the direction of the curresponding feature φ . Thus , the bonus α/√u represents the uncertainty along the direction of φ . By adding the bonus to Q-value , we obtain an optimistic value function Q+ , which serves as an upper bound of Q to encourage exploration . The bonus in each step is propagated from the end of the episode by the backward update of Q-value ( line 7-11 ) , which follows the principle of dynamic programming . Theoretical analysis ( Jin et al. , 2020 ) shows that optimistic LSVI achieves a near-optimal worst-case regret of Õ ( √ d3T 3L3 ) with proper selections of α and λ , where L is the total number of steps .

Summary: This paper studies optimistic exploration for deep reinforcement learning. They propose an algorithm called OEB3, which utilizes the disagreement of bootstrapping Q-functions as the confidence bonus to guide exploration. They show that their confidence bonus matches the confidence bonus of optimistic LSVI in linear setting. After that, they evaluate their algorithm on plenty of benchmark problems including Mnist maze and 49 Atari games. Their algorithm outperforms other SOTA exploration approaches.