EvIL: Evolution Strategies for Generalisable Imitation Learning

• URL: http://arxiv.org/abs/2406.11905v1
• Date: Sat, 15 Jun 2024 22:46:39 GMT
• Title: EvIL: Evolution Strategies for Generalisable Imitation Learning
• Authors: Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster,
• Abstract summary: In imitation learning (IL) expert demonstrations and the environment we want to deploy our learned policy in aren't exactly the same. Compared to policy-centric approaches to IL like cloning, reward-centric approaches like inverse reinforcement learning (IRL) often better replicate expert behaviour in new environments. We find that modern deep IL algorithms frequently recover rewards which induce policies far weaker than the expert, even in the same environment the demonstrations were collected in. We propose a novel evolution-strategies based method EvIL to optimise for a reward-shaping term that speeds up re-training in the target environment.
• Score: 33.745657379141676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Often times in imitation learning (IL), the environment we collect expert demonstrations in and the environment we want to deploy our learned policy in aren't exactly the same (e.g. demonstrations collected in simulation but deployment in the real world). Compared to policy-centric approaches to IL like behavioural cloning, reward-centric approaches like inverse reinforcement learning (IRL) often better replicate expert behaviour in new environments. This transfer is usually performed by optimising the recovered reward under the dynamics of the target environment. However, (a) we find that modern deep IL algorithms frequently recover rewards which induce policies far weaker than the expert, even in the same environment the demonstrations were collected in. Furthermore, (b) these rewards are often quite poorly shaped, necessitating extensive environment interaction to optimise effectively. We provide simple and scalable fixes to both of these concerns. For (a), we find that reward model ensembles combined with a slightly different training objective significantly improves re-training and transfer performance. For (b), we propose a novel evolution-strategies based method EvIL to optimise for a reward-shaping term that speeds up re-training in the target environment, closing a gap left open by the classical theory of IRL. On a suite of continuous control tasks, we are able to re-train policies in target (and source) environments more interaction-efficiently than prior work.

Related papers

• Survival of the Fittest: Evolutionary Adaptation of Policies for Environmental Shifts [0.15889427269227555]
  We develop an adaptive re-training algorithm inspired by evolutionary game theory (EGT) ERPO shows faster policy adaptation, higher average rewards, and reduced computational costs in policy adaptation.
  arXiv  Detail & Related papers  (2024-10-22T09:29:53Z)
• A Conservative Approach for Few-Shot Transfer in Off-Dynamics Reinforcement Learning [3.1515473193934778]
  Off-dynamics Reinforcement Learning seeks to transfer a policy from a source environment to a target environment characterized by distinct yet similar dynamics. We propose an innovative approach inspired by recent advancements in Imitation Learning and conservative RL algorithms.
  arXiv  Detail & Related papers  (2023-12-24T13:09:08Z)
• Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning [96.72185761508668]
  Planning at Test-time (IMPLANT) is a new meta-algorithm for imitation learning. We demonstrate that IMPLANT significantly outperforms benchmark imitation learning approaches on standard control environments.
  arXiv  Detail & Related papers  (2022-04-07T17:16:52Z)
• Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL [91.26538493552817]
  We present a formulation of hindsight relabeling for meta-RL, which relabels experience during meta-training to enable learning to learn entirely using sparse reward. We demonstrate the effectiveness of our approach on a suite of challenging sparse reward goal-reaching environments.
  arXiv  Detail & Related papers  (2021-12-02T00:51:17Z)
• OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching [12.335788185691916]
  Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedious. Prior IRL algorithms use on-policy transitions, which require intensive sampling from the current policy for stable and optimal performance. We present Off-Policy Inverse Reinforcement Learning (OPIRL), which adopts off-policy data distribution instead of on-policy.
  arXiv  Detail & Related papers  (2021-09-09T14:32:26Z)
• Self-Supervised Policy Adaptation during Deployment [98.25486842109936]
  Self-supervision allows the policy to continue training after deployment without using any rewards. Empirical evaluations are performed on diverse simulation environments from DeepMind Control suite and ViZDoom. Our method improves generalization in 31 out of 36 environments across various tasks and outperforms domain randomization on a majority of environments.
  arXiv  Detail & Related papers  (2020-07-08T17:56:27Z)
• Environment Shaping in Reinforcement Learning using State Abstraction [63.444831173608605]
  We propose a novel framework of emphenvironment shaping using state abstraction. Our key idea is to compress the environment's large state space with noisy signals to an abstracted space. We show that the agent's policy learnt in the shaped environment preserves near-optimal behavior in the original environment.
  arXiv  Detail & Related papers  (2020-06-23T17:00:22Z)
• Ecological Reinforcement Learning [76.9893572776141]
  We study the kinds of environment properties that can make learning under such conditions easier. understanding how properties of the environment impact the performance of reinforcement learning agents can help us to structure our tasks in ways that make learning tractable.
  arXiv  Detail & Related papers  (2020-06-22T17:55:03Z)
• Deep Reinforcement Learning amidst Lifelong Non-Stationarity [67.24635298387624]
  We show that an off-policy RL algorithm can reason about and tackle lifelong non-stationarity. Our method leverages latent variable models to learn a representation of the environment from current and past experiences. We also introduce several simulation environments that exhibit lifelong non-stationarity, and empirically find that our approach substantially outperforms approaches that do not reason about environment shift.
  arXiv  Detail & Related papers  (2020-06-18T17:34:50Z)
• Energy-Based Imitation Learning [29.55675131809474]
  We tackle a common scenario in imitation learning (IL) where agents try to recover the optimal policy from expert demonstrations. Inspired by recent progress in energy-based model (EBM), in this paper we propose a simplified IL framework named Energy-Based Imitation Learning (EBIL) EBIL combines the idea of both EBM and occupancy measure matching, and via theoretic analysis we reveal that EBIL and Max-Entropy IRL (MaxEnt IRL) approaches are two sides of the same coin.
  arXiv  Detail & Related papers  (2020-04-20T15:49:35Z)

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.