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While we primarily focus the analysis of DisCo in the tabular case, we believe that the formal definition of Autonomous eXploration problems and the general structure of DisCo may serve as a theoretical grounding of many recent approaches to unsupervised exploration

Improved Sample Complexity for Incremental Autonomous Exploration in MDPs

NIPS 2020, (2020)

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Abstract

We investigate the exploration of an unknown environment when no reward function is provided. Building on the incremental exploration setting introduced by Lim and Auer [1], we define the objective of learning the set of $\epsilon$-optimal goal-conditioned policies attaining all states that are incrementally reachable within $L$ steps (...More

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Introduction
  • In settings where the reward signal is not informative enough — e.g., too sparse, time-varying or even absent — a reinforcement learning (RL) agent needs to explore the environment driven by objectives other than reward maximization, see [e.g., 2, 3, 4, 5].
  • The authors strengthen the objective of incremental exploration and require the agent to learn "-optimal goal-conditioned policies for any L-controllable state.
Highlights
  • In settings where the reward signal is not informative enough — e.g., too sparse, time-varying or even absent — a reinforcement learning (RL) agent needs to explore the environment driven by objectives other than reward maximization, see [e.g., 2, 3, 4, 5]
  • While we primarily focus the analysis of DisCo in the tabular case, we believe that the formal definition of Autonomous eXploration (AX) problems and the general structure of DisCo may serve as a theoretical grounding of many recent approaches to unsupervised exploration
  • Go-Explore assumes that the world is deterministic and resettable, meaning that one can reset the state of the simulator to a previous visit to that cell
  • Very recently [14], the same authors proposed a way to relax this requirement by training goal-conditioned policies to reliably return to cells in the archive during the exploration phase
  • Interesting directions for future investigation include: 1) Deriving a lower bound for the AX problems; 2) Integrating DisCo into the meta-algorithm MNM [31] which deals with incremental exploration for AXL in non-stationary environments; 3) Extending the problem to continuous state space and function approximation; 4) Relaxing the definition of incrementally controllable states and relaxing the performance definition towards allowing the agent to have a non-zero but limited sample complexity of learning a shortest-path policy for any state at test time
Results
  • AXL is the original objective introduced in [1] and it requires the agent to discover all the incrementally L-controllable states as fast as possible.5 At the end of the learning process, for each state s the agent should return a policy that can reach s from s0 in at most steps.
  • The algorithm proceeds through rounds, which are indexed by k and incremented whenever a state in U gets transferred to the set K, i.e., when the transition model reaches a level of accuracy sufficient to compute a policy to control one of the states encountered before.
  • By considering a restricted set Wk ✓ Uk. if the estimated probability pbk of reaching a state s 2 Uk from any of the controllable states in Kk is lower than (1 "/2)/L, no shortest-path policy restricted on Kk could get to s from s0 in less than L + " steps on average.
  • Kk. Since optimistic p0 is unknown, k value iteration k (OVISSP) for SSP [25, 26] to obtain a value function ues0 and its associated greedy policy ⇡es0 restricted on Kk. The agent chooses a candidate goal state s† for which the value ue† := ues† (s0) is the smallest.
  • The algorithm terminates and, using the current estimates of the model, it recomputes an optimistic shortest-path policy ⇡s restricted on the final set KK for each state s 2 KK.
  • (and on the expansion of the set of the so far controllable states may alter and refine the optimal goal-reaching policies restricted on it.
  • 2.2, the better dependency on " both improves the quality of the output goal-reaching policies as well as reduces the number of incrementally (L + ")-controllable states returned by the algorithm.
Conclusion
  • The authors investigated the theoretical dimension of this direction, by provably learning such goal-conditioned policies for the set of incrementally controllable states.
  • Interesting directions for future investigation include: 1) Deriving a lower bound for the AX problems; 2) Integrating DisCo into the meta-algorithm MNM [31] which deals with incremental exploration for AXL in non-stationary environments; 3) Extending the problem to continuous state space and function approximation; 4) Relaxing the definition of incrementally controllable states and relaxing the performance definition towards allowing the agent to have a non-zero but limited sample complexity of learning a shortest-path policy for any state at test time.
Summary
  • In settings where the reward signal is not informative enough — e.g., too sparse, time-varying or even absent — a reinforcement learning (RL) agent needs to explore the environment driven by objectives other than reward maximization, see [e.g., 2, 3, 4, 5].
  • The authors strengthen the objective of incremental exploration and require the agent to learn "-optimal goal-conditioned policies for any L-controllable state.
  • AXL is the original objective introduced in [1] and it requires the agent to discover all the incrementally L-controllable states as fast as possible.5 At the end of the learning process, for each state s the agent should return a policy that can reach s from s0 in at most steps.
  • The algorithm proceeds through rounds, which are indexed by k and incremented whenever a state in U gets transferred to the set K, i.e., when the transition model reaches a level of accuracy sufficient to compute a policy to control one of the states encountered before.
  • By considering a restricted set Wk ✓ Uk. if the estimated probability pbk of reaching a state s 2 Uk from any of the controllable states in Kk is lower than (1 "/2)/L, no shortest-path policy restricted on Kk could get to s from s0 in less than L + " steps on average.
  • Kk. Since optimistic p0 is unknown, k value iteration k (OVISSP) for SSP [25, 26] to obtain a value function ues0 and its associated greedy policy ⇡es0 restricted on Kk. The agent chooses a candidate goal state s† for which the value ue† := ues† (s0) is the smallest.
  • The algorithm terminates and, using the current estimates of the model, it recomputes an optimistic shortest-path policy ⇡s restricted on the final set KK for each state s 2 KK.
  • (and on the expansion of the set of the so far controllable states may alter and refine the optimal goal-reaching policies restricted on it.
  • 2.2, the better dependency on " both improves the quality of the output goal-reaching policies as well as reduces the number of incrementally (L + ")-controllable states returned by the algorithm.
  • The authors investigated the theoretical dimension of this direction, by provably learning such goal-conditioned policies for the set of incrementally controllable states.
  • Interesting directions for future investigation include: 1) Deriving a lower bound for the AX problems; 2) Integrating DisCo into the meta-algorithm MNM [31] which deals with incremental exploration for AXL in non-stationary environments; 3) Extending the problem to continuous state space and function approximation; 4) Relaxing the definition of incrementally controllable states and relaxing the performance definition towards allowing the agent to have a non-zero but limited sample complexity of learning a shortest-path policy for any state at test time.
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