Multi-Stage Episodic Control for Strategic Exploration in Text Games

• URL: http://arxiv.org/abs/2201.01251v1
• Date: Tue, 4 Jan 2022 17:19:52 GMT
• Title: Multi-Stage Episodic Control for Strategic Exploration in Text Games
• Authors: Jens Tuyls, Shunyu Yao, Sham Kakade, Karthik Narasimhan
• Abstract summary: This work proposes to tackle the explore-vs-exploit dilemma using a multi-stage approach that explicitly disentangles these two strategies within each episode. Our algorithm, called eXploit-Then-eXplore (XTX), begins each episode using an exploitation policy that imitates a set of promising trajectories from the past. Our method significantly outperforms prior approaches by 27% and 11% average normalized score over 12 games from the Jericho benchmark.
• Score: 16.897326154822135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text adventure games present unique challenges to reinforcement learning methods due to their combinatorially large action spaces and sparse rewards. The interplay of these two factors is particularly demanding because large action spaces require extensive exploration, while sparse rewards provide limited feedback. This work proposes to tackle the explore-vs-exploit dilemma using a multi-stage approach that explicitly disentangles these two strategies within each episode. Our algorithm, called eXploit-Then-eXplore (XTX), begins each episode using an exploitation policy that imitates a set of promising trajectories from the past, and then switches over to an exploration policy aimed at discovering novel actions that lead to unseen state spaces. This policy decomposition allows us to combine global decisions about which parts of the game space to return to with curiosity-based local exploration in that space, motivated by how a human may approach these games. Our method significantly outperforms prior approaches by 27% and 11% average normalized score over 12 games from the Jericho benchmark (Hausknecht et al., 2020) in both deterministic and stochastic settings, respectively. On the game of Zork1, in particular, XTX obtains a score of 103, more than a 2x improvement over prior methods, and pushes past several known bottlenecks in the game that have plagued previous state-of-the-art methods.

Related papers

• MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization [91.80034860399677]
  Reinforcement learning algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. We introduce a framework, MaxInfoRL, for balancing intrinsic and extrinsic exploration. We show that our approach achieves sublinear regret in the simplified setting of multi-armed bandits.
  arXiv  Detail & Related papers  (2024-12-16T18:59:53Z)
• Deterministic Exploration via Stationary Bellman Error Maximization [6.474106100512158]
  Exploration is a crucial and distinctive aspect of reinforcement learning (RL) In this paper, we introduce three modifications to stabilize the latter and arrive at a deterministic exploration policy. Our experimental results show that our approach can outperform $varepsilon$-greedy in dense and sparse reward settings.
  arXiv  Detail & Related papers  (2024-10-31T11:46:48Z)
• Multi-Player Approaches for Dueling Bandits [58.442742345319225]
  We show that the direct use of a Follow Your Leader black-box approach matches the lower bound for this setting. We also analyze a message-passing fully distributed approach with a novel Condorcet-winner recommendation protocol.
  arXiv  Detail & Related papers  (2024-05-25T10:25:48Z)
• Long-Term Exploration in Persistent MDPs [68.8204255655161]
  We propose an exploration method called Rollback-Explore (RbExplore) In this paper, we propose an exploration method called Rollback-Explore (RbExplore), which utilizes the concept of the persistent Markov decision process. We test our algorithm in the hard-exploration Prince of Persia game, without rewards and domain knowledge.
  arXiv  Detail & Related papers  (2021-09-21T13:47:04Z)
• Generating Diverse and Competitive Play-Styles for Strategy Games [58.896302717975445]
  We propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) We show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.
  arXiv  Detail & Related papers  (2021-04-17T20:33:24Z)
• Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization [42.33734089361143]
  We propose a technique for discovering diverse strategic policies in complex multi-agent games. We derive a new algorithm, Reward-Randomized Policy Gradient (RPG) RPG is able to discover multiple distinctive human-interpretable strategies in challenging temporal trust dilemmas.
  arXiv  Detail & Related papers  (2021-03-08T06:26:55Z)
• BeBold: Exploration Beyond the Boundary of Explored Regions [66.88415950549556]
  In this paper, we propose the regulated difference of inverse visitation counts as a simple but effective criterion for intrinsic reward (IR) The criterion helps the agent explore Beyond the Boundary of explored regions and mitigates common issues in count-based methods, such as short-sightedness and detachment. The resulting method, BeBold, solves the 12 most challenging procedurally-generated tasks in MiniGrid with just 120M environment steps, without any curriculum learning.
  arXiv  Detail & Related papers  (2020-12-15T21:26:54Z)
• Efficient exploration of zero-sum stochastic games [83.28949556413717]
  We investigate the increasingly important and common game-solving setting where we do not have an explicit description of the game but only oracle access to it through gameplay. During a limited-duration learning phase, the algorithm can control the actions of both players in order to try to learn the game and how to play it well. Our motivation is to quickly learn strategies that have low exploitability in situations where evaluating the payoffs of a queried strategy profile is costly.
  arXiv  Detail & Related papers  (2020-02-24T20:30:38Z)
• How To Avoid Being Eaten By a Grue: Exploration Strategies for Text-Adventure Agents [17.215984752298443]
  We introduce two new game state exploration strategies for text-based games. We compare our exploration strategies against strong baselines on the classic text-adventure game, Zork1.
  arXiv  Detail & Related papers  (2020-02-19T17:18:20Z)
• Never Give Up: Learning Directed Exploration Strategies [63.19616370038824]
  We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic reward using k-nearest neighbors over the agent's recent experience to train the directed exploratory policies. A self-supervised inverse dynamics model is used to train the embeddings of the nearest neighbour lookup, biasing the novelty signal towards what the agent can control.
  arXiv  Detail & Related papers  (2020-02-14T13:57:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.